From c316b27a0b3afaa12af47a21d73ed8c8b59cd91c Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 26 Mar 2025 18:00:05 +0100 Subject: [PATCH 01/76] seperate data preparation and pipeline run --- .gitignore | 1 + ...x_pipeline_single_function_shard_map.ipynb | 332 ++++++++++++++++++ rubix/core/pipeline.py | 47 +-- 3 files changed, 359 insertions(+), 21 deletions(-) create mode 100644 notebooks/rubix_pipeline_single_function_shard_map.ipynb diff --git a/.gitignore b/.gitignore index 2f814b29..33b4ede5 100644 --- a/.gitignore +++ b/.gitignore @@ -173,3 +173,4 @@ rubix/spectra/ssp/templates/fsps.h5 notebooks/frames notebooks/frames/* +notebooks/nohup.out diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb new file mode 100644 index 00000000..b3e24706 --- /dev/null +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RUBIX pipeline\n", + "\n", + "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", + "\n", + "## How to use the Pipeline\n", + "1) Define a `config`\n", + "2) Setup the `pipeline yaml`\n", + "3) Run the RUBIX pipeline\n", + "4) Do science with the mock-data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Config\n", + "\n", + "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", + "\n", + "For the `config` you can choose the following options:\n", + "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", + "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", + "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", + "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", + "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", + "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", + "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", + "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", + "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", + "- `simulation - name`: currently only IllustrisTNG is supported\n", + "- `simulation - args - path`: where the data is stored and how the file will be named\n", + "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", + "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", + "- `telescope - psf`: define the point spread function that is applied to the mock data\n", + "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", + "- `telescope - noise`: define the noise that is applied to the mock data\n", + "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", + "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", + "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", + "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "config = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 14,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 1000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-14.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 1,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"FSPS\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Pipeline yaml\n", + "\n", + "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", + "\n", + "```yaml\n", + "calc_ifu:\n", + " Transformers:\n", + " rotate_galaxy:\n", + " name: rotate_galaxy\n", + " depends_on: null\n", + " args: []\n", + " kwargs:\n", + " type: \"face-on\"\n", + " filter_particles:\n", + " name: filter_particles\n", + " depends_on: rotate_galaxy\n", + " args: []\n", + " kwargs: {}\n", + " spaxel_assignment:\n", + " name: spaxel_assignment\n", + " depends_on: filter_particles\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " reshape_data:\n", + " name: reshape_data\n", + " depends_on: spaxel_assignment\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " calculate_spectra:\n", + " name: calculate_spectra\n", + " depends_on: reshape_data\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " scale_spectrum_by_mass:\n", + " name: scale_spectrum_by_mass\n", + " depends_on: calculate_spectra\n", + " args: []\n", + " kwargs: {}\n", + " doppler_shift_and_resampling:\n", + " name: doppler_shift_and_resampling\n", + " depends_on: scale_spectrum_by_mass\n", + " args: []\n", + " kwargs: {}\n", + " calculate_datacube:\n", + " name: calculate_datacube\n", + " depends_on: doppler_shift_and_resampling\n", + " args: []\n", + " kwargs: {}\n", + " convolve_psf:\n", + " name: convolve_psf\n", + " depends_on: calculate_datacube\n", + " args: []\n", + " kwargs: {}\n", + " convolve_lsf:\n", + " name: convolve_lsf\n", + " depends_on: convolve_psf\n", + " args: []\n", + " kwargs: {}\n", + " apply_noise:\n", + " name: apply_noise\n", + " depends_on: convolve_lsf\n", + " args: []\n", + " kwargs: {}\n", + "```\n", + "\n", + "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the pipeline\n", + "\n", + "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config)\n", + "\n", + "inputdata = pipe.prepare_data()\n", + "rubixdata = pipe.run(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Mock-data\n", + "\n", + "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import jax.numpy as jnp\n", + "\n", + "wave = pipe.telescope.wave_seq\n", + "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", + "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "spectra = rubixdata.stars.datacube # Spectra of all stars\n", + "print(spectra.shape)\n", + "\n", + "plt.plot(wave, spectra[12,12,:])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot a spacial image of the data cube" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "# get the spectra of the visible wavelengths from the ifu cube\n", + "visible_spectra = rubixdata.stars.datacube[:, :, visible_indices[0]]\n", + "#visible_spectra.shape\n", + "\n", + "# Sum up all spectra to create an image\n", + "image = jnp.sum(visible_spectra, axis = 2)\n", + "plt.imshow(image, origin=\"lower\", cmap=\"inferno\")\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DONE!\n", + "\n", + "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index e376bd4d..247fc038 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -37,15 +37,15 @@ class RubixPipeline: logger(Logger) : Logger instance for logging messages. ssp(object) : Stellar population synthesis model. telescope(object) : Telescope configuration. - data (dict): Dictionary containing particle data. + #data (dict): Dictionary containing particle data. func (callable): Compiled pipeline function to process data. Example -------- >>> from rubix.core.pipeline import RubixPipeline >>> config = "path/to/config.yml" - >>> pipeline = RubixPipeline(config) - >>> output = pipeline.run() + >>> inputdata = pipe.prepare_data() + >>> rubixdata = pipe.run(inputdata) >>> ssp_model = pipeline.ssp >>> telescope = pipeline.telescope """ @@ -56,10 +56,10 @@ def __init__(self, user_config: Union[dict, str]): self.logger = get_logger(self.user_config["logger"]) self.ssp = get_ssp(self.user_config) self.telescope = get_telescope(self.user_config) - self.data = self._prepare_data() + # self.data = self._prepare_data() self.func = None - def _prepare_data(self): + def prepare_data(self): """ Prepares and loads the data for the pipeline. @@ -135,10 +135,15 @@ def _get_pipeline_functions(self) -> list: return functions # TODO: currently returns dict, but later should return only the IFU cube - def run(self): + def run(self, inputdata): """ Runs the data processing pipeline. + Parameters + ---------- + input_data : dict + Data prepared from the `prepare_data` method. + Returns ------- dict @@ -161,7 +166,7 @@ def run(self): # Running the pipeline self.logger.info("Running the pipeline on the input data...") - output = self.func(self.data) + output = self.func(inputdata) block_until_ready(output) time_end = time.time() @@ -169,30 +174,30 @@ def run(self): "Pipeline run completed in %.2f seconds.", time_end - time_start ) - output.galaxy.redshift_unit = self.data.galaxy.redshift_unit - output.galaxy.center_unit = self.data.galaxy.center_unit - output.galaxy.halfmassrad_stars_unit = self.data.galaxy.halfmassrad_stars_unit + output.galaxy.redshift_unit = inputdata.galaxy.redshift_unit + output.galaxy.center_unit = inputdata.galaxy.center_unit + output.galaxy.halfmassrad_stars_unit = inputdata.galaxy.halfmassrad_stars_unit if output.stars.coords != None: - output.stars.coords_unit = self.data.stars.coords_unit - output.stars.velocity_unit = self.data.stars.velocity_unit - output.stars.mass_unit = self.data.stars.mass_unit + output.stars.coords_unit = inputdata.stars.coords_unit + output.stars.velocity_unit = inputdata.stars.velocity_unit + output.stars.mass_unit = inputdata.stars.mass_unit # output.stars.metallictiy_unit = self.data.stars.metallictiy_unit - output.stars.age_unit = self.data.stars.age_unit + output.stars.age_unit = inputdata.stars.age_unit output.stars.spatial_bin_edges_unit = "kpc" # output.stars.wavelength_unit = rubix_config["ssp"]["units"]["wavelength"] # output.stars.spectra_unit = rubix_config["ssp"]["units"]["flux"] # output.stars.datacube_unit = rubix_config["ssp"]["units"]["flux"] if output.gas.coords != None: - output.gas.coords_unit = self.data.gas.coords_unit - output.gas.velocity_unit = self.data.gas.velocity_unit - output.gas.mass_unit = self.data.gas.mass_unit - output.gas.density_unit = self.data.gas.density_unit - output.gas.internal_energy_unit = self.data.gas.internal_energy_unit + output.gas.coords_unit = inputdata.gas.coords_unit + output.gas.velocity_unit = inputdata.gas.velocity_unit + output.gas.mass_unit = inputdata.gas.mass_unit + output.gas.density_unit = inputdata.gas.density_unit + output.gas.internal_energy_unit = inputdata.gas.internal_energy_unit # output.gas.metallicity_unit = self.data.gas.metallicity_unit - output.gas.sfr_unit = self.data.gas.sfr_unit - output.gas.electron_abundance_unit = self.data.gas.electron_abundance_unit + output.gas.sfr_unit = inputdata.gas.sfr_unit + output.gas.electron_abundance_unit = inputdata.gas.electron_abundance_unit output.gas.spatial_bin_edges_unit = "kpc" # output.gas.wavelength_unit = rubix_config["ssp"]["units"]["wavelength"] # output.gas.spectra_unit = rubix_config["ssp"]["units"]["flux"] From cab76af86b2c69dcd140d362a1c334c41d9ecb1b Mon Sep 17 00:00:00 2001 From: anschaible Date: Fri, 28 Mar 2025 11:23:12 +0100 Subject: [PATCH 02/76] tests on sharding --- .../rubix_pipeline_single_function.ipynb | 9 +- ...x_pipeline_single_function_shard_map.ipynb | 10 + rubix/core/pipeline.py | 198 +++++++++++++----- 3 files changed, 158 insertions(+), 59 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function.ipynb b/notebooks/rubix_pipeline_single_function.ipynb index 46401f5d..f58971da 100644 --- a/notebooks/rubix_pipeline_single_function.ipynb +++ b/notebooks/rubix_pipeline_single_function.ipynb @@ -108,8 +108,15 @@ " \n", " \"ssp\": {\n", " \"template\": {\n", - " \"name\": \"BruzualCharlot2003\"\n", + " \"name\": \"FSPS\"\n", " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", " }, \n", "}" ] diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index b3e24706..67095920 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -229,6 +229,16 @@ "rubixdata = pipe.run(inputdata)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata = pipe.prepare_data()\n", + "shard_rubixdata = pipe.run_sharded(inputdata, shard_size=1000)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 247fc038..8f3c5305 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -1,10 +1,16 @@ import time +from types import SimpleNamespace from typing import Union import jax import jax.numpy as jnp + +# For shard_map and device mesh. +import numpy as np from beartype import beartype as typechecker from jax import block_until_ready +from jax.experimental import shard_map +from jax.sharding import Mesh, PartitionSpec as P from jaxtyping import jaxtyped from rubix.logger import get_logger @@ -31,23 +37,14 @@ class RubixPipeline: """ RubixPipeline is responsible for setting up and running the data processing pipeline. - Args: - user_config (dict or str): Parsed user configuration for the pipeline. - pipeline_config (dict): Configuration for the pipeline. - logger(Logger) : Logger instance for logging messages. - ssp(object) : Stellar population synthesis model. - telescope(object) : Telescope configuration. - #data (dict): Dictionary containing particle data. - func (callable): Compiled pipeline function to process data. - - Example - -------- - >>> from rubix.core.pipeline import RubixPipeline - >>> config = "path/to/config.yml" + Usage + ----- + >>> pipe = RubixPipeline(config) >>> inputdata = pipe.prepare_data() - >>> rubixdata = pipe.run(inputdata) - >>> ssp_model = pipeline.ssp - >>> telescope = pipeline.telescope + >>> # To run without sharding: + >>> output = pipe.run(inputdata) + >>> # To run with sharding using jax.shard_map: + >>> final_datacube = pipe.run_sharded(inputdata, shard_size=100000) """ def __init__(self, user_config: Union[dict, str]): @@ -56,7 +53,6 @@ def __init__(self, user_config: Union[dict, str]): self.logger = get_logger(self.user_config["logger"]) self.ssp = get_ssp(self.user_config) self.telescope = get_telescope(self.user_config) - # self.data = self._prepare_data() self.func = None def prepare_data(self): @@ -64,10 +60,9 @@ def prepare_data(self): Prepares and loads the data for the pipeline. Returns: - Dictionary containing particle data with keys: - 'n_particles', 'coords', 'velocities', 'metallicity', 'mass', and 'age'. + Object containing particle data with attributes such as: + 'coords', 'velocities', 'mass', 'age', and 'metallicity' under stars and gas. """ - # Get the data self.logger.info("Getting rubix data...") rubixdata = get_rubix_data(self.user_config) star_count = ( @@ -77,17 +72,6 @@ def prepare_data(self): self.logger.info( f"Data loaded with {star_count} star particles and {gas_count} gas particles." ) - # Setup the data dictionary - # TODO: This is a temporary solution, we need to figure out a better way to handle the data - # This works, because JAX can trace through the data dictionary - # Other option may be named tuples or data classes to have fixed keys - - # self.logger.debug("Data: %s", rubixdata) - # self.logger.debug( - # "Data Shape: %s", - # {k: v.shape for k, v in rubixdata.items() if hasattr(v, "shape")}, - # ) - return rubixdata @jaxtyped(typechecker=typechecker) @@ -101,7 +85,6 @@ def _get_pipeline_functions(self) -> list: self.logger.info("Setting up the pipeline...") self.logger.debug("Pipeline Configuration: %s", self.pipeline_config) - # TODO: maybe there is a nicer way to load the functions from the yaml config? rotate_galaxy = get_galaxy_rotation(self.user_config) filter_particles = get_filter_particles(self.user_config) spaxel_assignment = get_spaxel_assignment(self.user_config) @@ -131,83 +114,182 @@ def _get_pipeline_functions(self) -> list: convolve_lsf, apply_noise, ] - return functions - # TODO: currently returns dict, but later should return only the IFU cube def run(self, inputdata): """ - Runs the data processing pipeline. + Runs the data processing pipeline on the complete input data. Parameters ---------- - input_data : dict + inputdata : object Data prepared from the `prepare_data` method. Returns ------- - dict - Output of the pipeline after processing the input data. + object + Pipeline output (which includes the datacube and unit attributes). """ - # Create the pipeline time_start = time.time() functions = self._get_pipeline_functions() self._pipeline = pipeline.LinearTransformerPipeline( self.pipeline_config, functions ) - - # Assembling the pipeline self.logger.info("Assembling the pipeline...") self._pipeline.assemble() - - # Compiling the expressions self.logger.info("Compiling the expressions...") self.func = self._pipeline.compile_expression() - - # Running the pipeline self.logger.info("Running the pipeline on the input data...") output = self.func(inputdata) - block_until_ready(output) time_end = time.time() self.logger.info( "Pipeline run completed in %.2f seconds.", time_end - time_start ) + # Propagate unit attributes from input to output. output.galaxy.redshift_unit = inputdata.galaxy.redshift_unit output.galaxy.center_unit = inputdata.galaxy.center_unit output.galaxy.halfmassrad_stars_unit = inputdata.galaxy.halfmassrad_stars_unit - if output.stars.coords != None: + if output.stars.coords is not None: output.stars.coords_unit = inputdata.stars.coords_unit output.stars.velocity_unit = inputdata.stars.velocity_unit output.stars.mass_unit = inputdata.stars.mass_unit - # output.stars.metallictiy_unit = self.data.stars.metallictiy_unit output.stars.age_unit = inputdata.stars.age_unit output.stars.spatial_bin_edges_unit = "kpc" - # output.stars.wavelength_unit = rubix_config["ssp"]["units"]["wavelength"] - # output.stars.spectra_unit = rubix_config["ssp"]["units"]["flux"] - # output.stars.datacube_unit = rubix_config["ssp"]["units"]["flux"] - if output.gas.coords != None: + if output.gas.coords is not None: output.gas.coords_unit = inputdata.gas.coords_unit output.gas.velocity_unit = inputdata.gas.velocity_unit output.gas.mass_unit = inputdata.gas.mass_unit output.gas.density_unit = inputdata.gas.density_unit output.gas.internal_energy_unit = inputdata.gas.internal_energy_unit - # output.gas.metallicity_unit = self.data.gas.metallicity_unit output.gas.sfr_unit = inputdata.gas.sfr_unit output.gas.electron_abundance_unit = inputdata.gas.electron_abundance_unit output.gas.spatial_bin_edges_unit = "kpc" - # output.gas.wavelength_unit = rubix_config["ssp"]["units"]["wavelength"] - # output.gas.spectra_unit = rubix_config["ssp"]["units"]["flux"] - # output.gas.datacube_unit = rubix_config["ssp"]["units"]["flux"] return output - # TODO: implement gradient calculation + def run_sharded(self, inputdata, shard_size=100000): + """ + Runs the pipeline on sharded input data in parallel using jax.shard_map. + It splits the particle arrays (e.g. under stars and gas) into shards, runs + the compiled pipeline on each shard, and then combines the resulting datacubes. + + Parameters + ---------- + inputdata : object + Data prepared from the `prepare_data` method. + shard_size : int + Number of particles per shard. + + Returns + ------- + jax.numpy.ndarray + The final datacube combined from all shards. + """ + time_start = time.time() + # Assemble and compile the pipeline as before. + functions = self._get_pipeline_functions() + self._pipeline = pipeline.LinearTransformerPipeline( + self.pipeline_config, functions + ) + self.logger.info("Assembling the pipeline...") + self._pipeline.assemble() + self.logger.info("Compiling the expressions...") + self.func = self._pipeline.compile_expression() + + # --- Helper: Shard the particle data --- + def shard_subdata(subdata): + # subdata is expected to be a SimpleNamespace with attributes that are arrays. + new_subdata = {} + for attr, value in vars(subdata).items(): + if hasattr(value, "shape"): + n_particles = value.shape[0] + n_shards = n_particles // shard_size + # Truncate if needed. + new_value = value[: n_shards * shard_size] + # Reshape so that the first dimension indexes shards. + new_subdata[attr] = new_value.reshape( + (n_shards, shard_size) + value.shape[1:] + ) + else: + new_subdata[attr] = value + return SimpleNamespace(**new_subdata) + + # Create a new sharded input object. + sharded_input = {} + # Assume that 'stars' and 'gas' contain particle data. + for key in ["stars", "gas"]: + if hasattr(inputdata, key): + sharded_input[key] = shard_subdata(getattr(inputdata, key)) + # Preserve other parts (e.g. galaxy and units) as-is. + for key in vars(inputdata): + if key not in sharded_input: + sharded_input[key] = getattr(inputdata, key) + sharded_input = SimpleNamespace(**sharded_input) + # ----------------------------------------- + + # Determine the number of shards from one batched array (e.g. stars.coords). + n_shards = sharded_input.stars.coords.shape[0] + devices = np.array(jax.devices()) + if n_shards != devices.shape[0]: + raise ValueError( + f"Number of shards ({n_shards}) must equal number of devices ({devices.shape[0]})." + ) + mesh = Mesh(devices, ("x",)) + + # Define a function that will process one shard. + def pipeline_shard_fn(shard_input): + # Here, shard_input is a dict (or nested namespace) for one shard. + output = self.func(shard_input) + # Assume output has a 'datacube' attribute. + return output.datacube + + # Convert the sharded input namespace to a dict. + def to_dict(ns): + if isinstance(ns, SimpleNamespace): + return {k: to_dict(v) for k, v in vars(ns).items()} + else: + return ns + + sharded_input_dict = to_dict(sharded_input) + + # Create partitioning specifications for all array leaves. + def create_sharding_spec(val): + if hasattr(val, "shape") and isinstance(val, jnp.ndarray): + return P("x") + elif isinstance(val, dict): + return {k: create_sharding_spec(v) for k, v in val.items()} + else: + return None + + in_shardings = jax.tree_util.tree_map(create_sharding_spec, sharded_input_dict) + # Assume output datacube is sharded along 'x'. + out_shardings = P("x") + + # Use jax.shard_map to parallelize across shards. + sharded_pipeline_fn = shard_map.shard_map( + pipeline_shard_fn, + in_shardings, + out_shardings, + mesh, + ) + + with mesh: + sharded_datacubes = sharded_pipeline_fn(sharded_input_dict) + + # Combine the datacubes (here, by summing over the shard axis). + final_datacube = jnp.sum(sharded_datacubes, axis=0) + time_end = time.time() + self.logger.info( + "Sharded pipeline run completed in %.2f seconds.", time_end - time_start + ) + return final_datacube + def gradient(self): """ - This function will calculate the gradient of the pipeline, but is yet not implemented. + This function will calculate the gradient of the pipeline, but is not implemented. """ raise NotImplementedError("Gradient calculation is not implemented yet") From a622e284b64ab55fc2c38df5b900593f12c443b0 Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 15 Apr 2025 16:50:17 +0200 Subject: [PATCH 03/76] issues with sharding galaxy --- ...x_pipeline_single_function_shard_map.ipynb | 106 +++++++++++++++--- rubix/core/pipeline.py | 9 +- 2 files changed, 98 insertions(+), 17 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 67095920..246bcd0c 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,13 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# NBVAL_SKIP\n", "import os\n", - "os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'" + "#os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" ] }, { @@ -59,9 +60,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-15 16:49:06,526 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-04-15 16:49:06,530 - rubix - INFO - Rubix version: 0.0.post238+gf560922\n", + "2025-04-15 16:49:06,531 - rubix - INFO - JAX version: 0.5.0\n", + "2025-04-15 16:49:06,532 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5), CudaDevice(id=6), CudaDevice(id=7)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -92,7 +110,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 1000,\n", + " \"subset_size\": 8000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -218,22 +236,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config)\n", "\n", - "inputdata = pipe.prepare_data()\n", - "rubixdata = pipe.run(inputdata)" + "#inputdata = pipe.prepare_data()\n", + "#rubixdata = pipe.run(inputdata)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-15 16:49:07,683 - rubix - INFO - Getting rubix data...\n", + "2025-04-15 16:49:07,686 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-15 16:49:07,785 - rubix - INFO - Centering stars particles\n", + "2025-04-15 16:49:10,736 - rubix - WARNING - The Subset value is set in config. Using only subset of size 8000 for stars\n", + "2025-04-15 16:49:10,739 - rubix - INFO - Data loaded with 8000 star particles and 0 gas particles.\n", + "2025-04-15 16:49:10,739 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-15 16:49:10,740 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'reshape_data': {'name': 'reshape_data', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'reshape_data', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-15 16:49:10,743 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-15 16:49:10,745 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-15 16:49:10,772 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-15 16:49:11,371 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-15 16:49:11,392 - rubix - INFO - Getting cosmology...\n", + "2025-04-15 16:49:11,496 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-15 16:49:11,595 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-15 16:49:11,797 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-15 16:49:11,824 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-15 16:49:12,373 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-15 16:49:12,374 - rubix - INFO - Compiling the expressions...\n" + ] + }, + { + "ename": "ValueError", + "evalue": "pytree structure error: different types at key path\n shard_map in_specs\nAt that key path, the prefix pytree shard_map in_specs has a subtree of type\n \nbut at the same key path the full pytree has a subtree of different type\n .", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m inputdata \u001b[38;5;241m=\u001b[39m pipe\u001b[38;5;241m.\u001b[39mprepare_data()\n\u001b[0;32m----> 2\u001b[0m shard_rubixdata \u001b[38;5;241m=\u001b[39m \u001b[43mpipe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_sharded\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshard_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/rubix/rubix/core/pipeline.py:282\u001b[0m, in \u001b[0;36mRubixPipeline.run_sharded\u001b[0;34m(self, inputdata, shard_size)\u001b[0m\n\u001b[1;32m 274\u001b[0m sharded_pipeline_fn \u001b[38;5;241m=\u001b[39m shard_map\u001b[38;5;241m.\u001b[39mshard_map(\n\u001b[1;32m 275\u001b[0m pipeline_shard_fn,\n\u001b[1;32m 276\u001b[0m mesh,\n\u001b[1;32m 277\u001b[0m in_shardings,\n\u001b[1;32m 278\u001b[0m out_shardings,\n\u001b[1;32m 279\u001b[0m )\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m mesh:\n\u001b[0;32m--> 282\u001b[0m sharded_datacubes \u001b[38;5;241m=\u001b[39m \u001b[43msharded_pipeline_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43msharded_input_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;66;03m# Combine the datacubes (here, by summing over the shard axis).\u001b[39;00m\n\u001b[1;32m 285\u001b[0m final_datacube \u001b[38;5;241m=\u001b[39m jnp\u001b[38;5;241m.\u001b[39msum(sharded_datacubes, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/rubix/lib/python3.11/site-packages/jax/experimental/shard_map.py:171\u001b[0m, in \u001b[0;36m_shard_map..wrapped\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m 170\u001b[0m e, \u001b[38;5;241m*\u001b[39m_ \u001b[38;5;241m=\u001b[39m prefix_errors(in_specs, args)\n\u001b[0;32m--> 171\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshard_map in_specs\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 172\u001b[0m dyn_argnums, in_specs_flat \u001b[38;5;241m=\u001b[39m unzip2((i, s) \u001b[38;5;28;01mfor\u001b[39;00m i, s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(in_specs_flat)\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 174\u001b[0m fun, args_flat \u001b[38;5;241m=\u001b[39m argnums_partial(fun, dyn_argnums, args_flat, \u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "\u001b[0;31mValueError\u001b[0m: pytree structure error: different types at key path\n shard_map in_specs\nAt that key path, the prefix pytree shard_map in_specs has a subtree of type\n \nbut at the same key path the full pytree has a subtree of different type\n ." + ] + } + ], "source": [ "inputdata = pipe.prepare_data()\n", "shard_rubixdata = pipe.run_sharded(inputdata, shard_size=1000)" @@ -320,7 +400,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "rubix", "language": "python", "name": "python3" }, @@ -334,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.2" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 8f3c5305..f3e6e537 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -225,9 +225,10 @@ def shard_subdata(subdata): if hasattr(inputdata, key): sharded_input[key] = shard_subdata(getattr(inputdata, key)) # Preserve other parts (e.g. galaxy and units) as-is. - for key in vars(inputdata): - if key not in sharded_input: - sharded_input[key] = getattr(inputdata, key) + #sharded_input["galaxy"] = inputdata.galaxy + #for key in vars(inputdata): + # if key not in sharded_input: + # sharded_input[key] = getattr(inputdata, key) sharded_input = SimpleNamespace(**sharded_input) # ----------------------------------------- @@ -272,9 +273,9 @@ def create_sharding_spec(val): # Use jax.shard_map to parallelize across shards. sharded_pipeline_fn = shard_map.shard_map( pipeline_shard_fn, + mesh, in_shardings, out_shardings, - mesh, ) with mesh: From 9d013196a93c7434225edb6194b6141165a8ce81 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 23 Apr 2025 09:55:10 +0200 Subject: [PATCH 04/76] get rid of reshape data, pmap and the extra dimension in the arrays --- ...x_pipeline_single_function_shard_map.ipynb | 255 +++++++++++++++++- rubix/config/pipeline_config.yml | 8 +- rubix/core/ifu.py | 69 +++-- rubix/core/pipeline.py | 4 +- 4 files changed, 293 insertions(+), 43 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 246bcd0c..6bb71cd4 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -8,8 +8,14 @@ "source": [ "# NBVAL_SKIP\n", "import os\n", +<<<<<<< HEAD "#os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" +======= + "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", + "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'" +>>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) ] }, { @@ -67,16 +73,26 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD "2025-04-15 16:49:06,526 - rubix - INFO - \n", +======= + "2025-04-23 09:53:45,452 - rubix - INFO - \n", +>>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", +<<<<<<< HEAD "2025-04-15 16:49:06,530 - rubix - INFO - Rubix version: 0.0.post238+gf560922\n", "2025-04-15 16:49:06,531 - rubix - INFO - JAX version: 0.5.0\n", "2025-04-15 16:49:06,532 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5), CudaDevice(id=6), CudaDevice(id=7)] devices\n" +======= + "2025-04-23 09:53:45,453 - rubix - INFO - Rubix version: 0.0.post400+gee789d5.d20250306\n", + "2025-04-23 09:53:45,453 - rubix - INFO - JAX version: 0.5.0\n", + "2025-04-23 09:53:45,453 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" +>>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) ] } ], @@ -243,8 +259,62 @@ "name": "stderr", "output_type": "stream", "text": [ +<<<<<<< HEAD "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n" +======= + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 09:53:45,882 - rubix - INFO - Getting rubix data...\n", + "2025-04-23 09:53:45,883 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-23 09:53:45,943 - rubix - INFO - Centering stars particles\n", + "2025-04-23 09:53:46,433 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", + "2025-04-23 09:53:46,435 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", + "2025-04-23 09:53:46,435 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-23 09:53:46,435 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-23 09:53:46,436 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-23 09:53:46,437 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 09:53:46,444 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 09:53:46,587 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 09:53:46,594 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 09:53:46,618 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 09:53:46,636 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 09:53:46,674 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 09:53:46,681 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 09:53:46,848 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-23 09:53:46,848 - rubix - INFO - Compiling the expressions...\n", + "2025-04-23 09:53:46,848 - rubix - INFO - Running the pipeline on the input data...\n", + "2025-04-23 09:53:46,858 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-23 09:53:46,900 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-23 09:53:46,903 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-23 09:53:46,910 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-23 09:53:46,910 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-23 09:53:47,010 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-23 09:53:47,010 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-23 09:53:47,012 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-23 09:53:47,012 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-23 09:53:47,012 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-23 09:53:47,040 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-23 09:53:47,041 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-23 09:53:47,041 - rubix - INFO - Convolving with PSF...\n", + "2025-04-23 09:53:47,043 - rubix - INFO - Convolving with LSF...\n", + "2025-04-23 09:53:47,045 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "2025-04-23 09:53:48,780 - rubix - INFO - Pipeline run completed in 2.34 seconds.\n" +>>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) ] } ], @@ -315,8 +385,8 @@ } ], "source": [ - "inputdata = pipe.prepare_data()\n", - "shard_rubixdata = pipe.run_sharded(inputdata, shard_size=1000)" + "#inputdata = pipe.prepare_data()\n", + "#shard_rubixdata = pipe.run_sharded(inputdata, shard_size=1000)" ] }, { @@ -330,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -351,9 +421,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25, 25, 3721)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", - "visible_spectra = rubixdata.stars.datacube[:, :, visible_indices[0]]\n", + "visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", "#visible_spectra.shape\n", "\n", "# Sum up all spectra to create an image\n", @@ -414,7 +649,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", +<<<<<<< HEAD "version": "3.11.9" +======= + "version": "3.12.8" +>>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) } }, "nbformat": 4, diff --git a/rubix/config/pipeline_config.yml b/rubix/config/pipeline_config.yml index 19b02776..450369c2 100644 --- a/rubix/config/pipeline_config.yml +++ b/rubix/config/pipeline_config.yml @@ -16,15 +16,9 @@ calc_ifu: args: [] kwargs: {} - reshape_data: - name: reshape_data - depends_on: spaxel_assignment - args: [] - kwargs: {} - calculate_spectra: name: calculate_spectra - depends_on: reshape_data + depends_on: spaxel_assignment args: [] kwargs: {} diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 38e5edb5..76172aaa 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -94,7 +94,7 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: """ # Define the chunk size (number of particles per chunk) chunk_size = 250000 - total_length = metallicity[0].shape[ + total_length = metallicity.shape[ 0 ] # assuming metallicity[0] is your 1D array of particles @@ -105,8 +105,8 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: for start in range(0, total_length, chunk_size): end = min(start + chunk_size, total_length) current_chunk = lookup_interpolation( - metallicity[0][start:end], - age[0][start:end], + metallicity[start:end], + age[start:end], ) spectra_chunks.append(current_chunk) @@ -114,7 +114,7 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: spectra = jnp.concatenate(spectra_chunks, axis=0) logger.debug(f"Calculation Finished! Spectra shape: {spectra.shape}") spectra_jax = jnp.array(spectra) - spectra_jax = jnp.expand_dims(spectra_jax, axis=0) + #spectra_jax = jnp.expand_dims(spectra_jax, axis=0) rubixdata.stars.spectra = spectra_jax # setattr(rubixdata.gas, "spectra", spectra) # jax.debug.print("Calculate Spectra: Spectra {}", spectra) @@ -184,19 +184,19 @@ def resample_spectrum_vmap(initial_spectrum, initial_wavelength): # Parallelize the vectorized function across devices -@jaxtyped(typechecker=typechecker) -def get_resample_spectrum_pmap(target_wavelength) -> Callable: - """ - Pmap the function that resamples the spectra of the stars to the telescope wavelength grid. +#@jaxtyped(typechecker=typechecker) +#def get_resample_spectrum_pmap(target_wavelength) -> Callable: +# """ +# Pmap the function that resamples the spectra of the stars to the telescope wavelength grid. - Args: - target_wavelength (jax.Array): The telescope wavelength grid +# Args: +# target_wavelength (jax.Array): The telescope wavelength grid - Returns: - The function that resamples the spectra to the telescope wavelength grid. - """ - vmapped_resample_spectrum = get_resample_spectrum_vmap(target_wavelength) - return jax.pmap(vmapped_resample_spectrum) +# Returns: +# The function that resamples the spectra to the telescope wavelength grid. +# """ +# vmapped_resample_spectrum = get_resample_spectrum_vmap(target_wavelength) +# return jax.pmap(vmapped_resample_spectrum) @jaxtyped(typechecker=typechecker) @@ -214,12 +214,20 @@ def get_velocities_doppler_shift_vmap( The function that doppler shifts the wavelength based on the velocity of the stars. """ - def func(velocity): + #def func(velocity): + # return velocity_doppler_shift( + # wavelength=ssp_wave, velocity=velocity, direction=velocity_direction + # ) + + #return jax.vmap(func, in_axes=0) + def doppler_fn(velocities): return velocity_doppler_shift( - wavelength=ssp_wave, velocity=velocity, direction=velocity_direction + wavelength=ssp_wave, + velocity=velocities, + direction=velocity_direction, ) - return jax.vmap(func, in_axes=0) + return doppler_fn @jaxtyped(typechecker=typechecker) @@ -277,8 +285,12 @@ def process_particle( logger.debug(f"Telescope Wave Seq: {telescope_wavelength.shape}") # Function to resample the spectrum to the telescope wavelength grid - resample_spectrum_pmap = get_resample_spectrum_pmap(telescope_wavelength) - spectrum_resampled = resample_spectrum_pmap( + #resample_spectrum_pmap = get_resample_spectrum_pmap(telescope_wavelength) + #spectrum_resampled = resample_spectrum_pmap( + # particle.spectra, doppler_shifted_ssp_wave + #) + resample_fn = get_resample_spectrum_vmap(telescope_wavelength) + spectrum_resampled = resample_fn( particle.spectra, doppler_shifted_ssp_wave ) return spectrum_resampled @@ -320,17 +332,22 @@ def get_calculate_datacube(config: dict) -> Callable: num_spaxels = int(telescope.sbin) # Bind the num_spaxels to the function - calculate_cube_fn = jax.tree_util.Partial(calculate_cube, num_spaxels=num_spaxels) - calculate_cube_pmap = jax.pmap(calculate_cube_fn) + #calculate_cube_fn = jax.tree_util.Partial(calculate_cube, num_spaxels=num_spaxels) + #calculate_cube_pmap = jax.pmap(calculate_cube_fn) @jaxtyped(typechecker=typechecker) def calculate_datacube(rubixdata: RubixData) -> RubixData: logger.info("Calculating Data Cube...") - ifu_cubes = calculate_cube_pmap( - spectra=rubixdata.stars.spectra, - spaxel_index=rubixdata.stars.pixel_assignment, + #ifu_cubes = calculate_cube_fn( + # spectra=rubixdata.stars.spectra, + # spaxel_index=rubixdata.stars.pixel_assignment, + #) + datacube = calculate_cube( + rubixdata.stars.spectra, + rubixdata.stars.pixel_assignment, + num_spaxels ) - datacube = jnp.sum(ifu_cubes, axis=0) + #datacube = jnp.sum(ifu_cubes, axis=0) logger.debug(f"Datacube Shape: {datacube.shape}") # logger.debug(f"This is the datacube: {datacube}") datacube_jax = jnp.array(datacube) diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index f3e6e537..310aae30 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -89,7 +89,7 @@ def _get_pipeline_functions(self) -> list: filter_particles = get_filter_particles(self.user_config) spaxel_assignment = get_spaxel_assignment(self.user_config) calculate_spectra = get_calculate_spectra(self.user_config) - reshape_data = get_reshape_data(self.user_config) + #reshape_data = get_reshape_data(self.user_config) scale_spectrum_by_mass = get_scale_spectrum_by_mass(self.user_config) doppler_shift_and_resampling = get_doppler_shift_and_resampling( self.user_config @@ -105,7 +105,7 @@ def _get_pipeline_functions(self) -> list: filter_particles, spaxel_assignment, calculate_spectra, - reshape_data, + #reshape_data, scale_spectrum_by_mass, doppler_shift_and_resampling, apply_extinction, From f599f73cb2a654f3ca75fc3ec390268cba3350e8 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 23 Apr 2025 10:04:54 +0200 Subject: [PATCH 05/76] remove chunking of particles for spectral lookup --- ...x_pipeline_single_function_shard_map.ipynb | 312 +++--------------- rubix/core/ifu.py | 49 +-- 2 files changed, 69 insertions(+), 292 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 6bb71cd4..1269d21e 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,20 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# NBVAL_SKIP\n", "import os\n", -<<<<<<< HEAD - "#os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", - "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" -======= "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'" ->>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) ] }, { @@ -66,36 +61,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ -<<<<<<< HEAD - "2025-04-15 16:49:06,526 - rubix - INFO - \n", -======= - "2025-04-23 09:53:45,452 - rubix - INFO - \n", ->>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", -<<<<<<< HEAD - "2025-04-15 16:49:06,530 - rubix - INFO - Rubix version: 0.0.post238+gf560922\n", - "2025-04-15 16:49:06,531 - rubix - INFO - JAX version: 0.5.0\n", - "2025-04-15 16:49:06,532 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5), CudaDevice(id=6), CudaDevice(id=7)] devices\n" -======= - "2025-04-23 09:53:45,453 - rubix - INFO - Rubix version: 0.0.post400+gee789d5.d20250306\n", - "2025-04-23 09:53:45,453 - rubix - INFO - JAX version: 0.5.0\n", - "2025-04-23 09:53:45,453 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" ->>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -126,7 +94,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 8000,\n", + " \"subset_size\": 1000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -252,69 +220,64 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ -<<<<<<< HEAD - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" -======= "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:53:45,882 - rubix - INFO - Getting rubix data...\n", - "2025-04-23 09:53:45,883 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-23 09:53:45,943 - rubix - INFO - Centering stars particles\n", - "2025-04-23 09:53:46,433 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", - "2025-04-23 09:53:46,435 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", - "2025-04-23 09:53:46,435 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-23 09:53:46,435 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-23 09:53:46,436 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-23 09:53:46,437 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-23 09:55:39,695 - rubix - INFO - Getting rubix data...\n", + "2025-04-23 09:55:39,696 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-23 09:55:39,739 - rubix - INFO - Centering stars particles\n", + "2025-04-23 09:55:39,951 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", + "2025-04-23 09:55:39,952 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", + "2025-04-23 09:55:39,953 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-23 09:55:39,953 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-23 09:55:39,953 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-23 09:55:39,954 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:53:46,444 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 09:55:39,961 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:53:46,587 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-23 09:55:39,969 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:53:46,594 - rubix - INFO - Getting cosmology...\n", - "2025-04-23 09:53:46,618 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-23 09:53:46,636 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 09:55:39,976 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 09:55:40,005 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 09:55:40,019 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:53:46,674 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-23 09:55:40,047 - rubix - DEBUG - SSP Wave: (5994,)\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:53:46,681 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 09:55:40,056 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:53:46,848 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-23 09:53:46,848 - rubix - INFO - Compiling the expressions...\n", - "2025-04-23 09:53:46,848 - rubix - INFO - Running the pipeline on the input data...\n", - "2025-04-23 09:53:46,858 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-23 09:53:46,900 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-23 09:53:46,903 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-23 09:53:46,910 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-23 09:53:46,910 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-23 09:53:47,010 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-23 09:53:47,010 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-23 09:53:47,012 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-23 09:53:47,012 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-23 09:53:47,012 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-23 09:53:47,040 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-23 09:53:47,041 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-23 09:53:47,041 - rubix - INFO - Convolving with PSF...\n", - "2025-04-23 09:53:47,043 - rubix - INFO - Convolving with LSF...\n", - "2025-04-23 09:53:47,045 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", - "2025-04-23 09:53:48,780 - rubix - INFO - Pipeline run completed in 2.34 seconds.\n" ->>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) + "2025-04-23 09:55:40,072 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-23 09:55:40,072 - rubix - INFO - Compiling the expressions...\n", + "2025-04-23 09:55:40,072 - rubix - INFO - Running the pipeline on the input data...\n", + "2025-04-23 09:55:40,079 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-23 09:55:40,112 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-23 09:55:40,113 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-23 09:55:40,114 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-23 09:55:40,114 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-23 09:55:40,115 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-23 09:55:40,116 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-23 09:55:40,117 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-23 09:55:40,117 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-23 09:55:40,117 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-23 09:55:40,119 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-23 09:55:40,120 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-23 09:55:40,120 - rubix - INFO - Convolving with PSF...\n", + "2025-04-23 09:55:40,122 - rubix - INFO - Convolving with LSF...\n", + "2025-04-23 09:55:40,132 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "2025-04-23 09:55:41,965 - rubix - INFO - Pipeline run completed in 2.01 seconds.\n" ] } ], @@ -322,68 +285,15 @@ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config)\n", "\n", - "#inputdata = pipe.prepare_data()\n", - "#rubixdata = pipe.run(inputdata)" + "inputdata = pipe.prepare_data()\n", + "rubixdata = pipe.run(inputdata)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-15 16:49:07,683 - rubix - INFO - Getting rubix data...\n", - "2025-04-15 16:49:07,686 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-15 16:49:07,785 - rubix - INFO - Centering stars particles\n", - "2025-04-15 16:49:10,736 - rubix - WARNING - The Subset value is set in config. Using only subset of size 8000 for stars\n", - "2025-04-15 16:49:10,739 - rubix - INFO - Data loaded with 8000 star particles and 0 gas particles.\n", - "2025-04-15 16:49:10,739 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-15 16:49:10,740 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'reshape_data': {'name': 'reshape_data', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'reshape_data', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-15 16:49:10,743 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-15 16:49:10,745 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-15 16:49:10,772 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-15 16:49:11,371 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-15 16:49:11,392 - rubix - INFO - Getting cosmology...\n", - "2025-04-15 16:49:11,496 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-15 16:49:11,595 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-15 16:49:11,797 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-15 16:49:11,824 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-15 16:49:12,373 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-15 16:49:12,374 - rubix - INFO - Compiling the expressions...\n" - ] - }, - { - "ename": "ValueError", - "evalue": "pytree structure error: different types at key path\n shard_map in_specs\nAt that key path, the prefix pytree shard_map in_specs has a subtree of type\n \nbut at the same key path the full pytree has a subtree of different type\n .", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m inputdata \u001b[38;5;241m=\u001b[39m pipe\u001b[38;5;241m.\u001b[39mprepare_data()\n\u001b[0;32m----> 2\u001b[0m shard_rubixdata \u001b[38;5;241m=\u001b[39m \u001b[43mpipe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_sharded\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshard_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/rubix/rubix/core/pipeline.py:282\u001b[0m, in \u001b[0;36mRubixPipeline.run_sharded\u001b[0;34m(self, inputdata, shard_size)\u001b[0m\n\u001b[1;32m 274\u001b[0m sharded_pipeline_fn \u001b[38;5;241m=\u001b[39m shard_map\u001b[38;5;241m.\u001b[39mshard_map(\n\u001b[1;32m 275\u001b[0m pipeline_shard_fn,\n\u001b[1;32m 276\u001b[0m mesh,\n\u001b[1;32m 277\u001b[0m in_shardings,\n\u001b[1;32m 278\u001b[0m out_shardings,\n\u001b[1;32m 279\u001b[0m )\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m mesh:\n\u001b[0;32m--> 282\u001b[0m sharded_datacubes \u001b[38;5;241m=\u001b[39m \u001b[43msharded_pipeline_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43msharded_input_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;66;03m# Combine the datacubes (here, by summing over the shard axis).\u001b[39;00m\n\u001b[1;32m 285\u001b[0m final_datacube \u001b[38;5;241m=\u001b[39m jnp\u001b[38;5;241m.\u001b[39msum(sharded_datacubes, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n", - " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/rubix/lib/python3.11/site-packages/jax/experimental/shard_map.py:171\u001b[0m, in \u001b[0;36m_shard_map..wrapped\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m 170\u001b[0m e, \u001b[38;5;241m*\u001b[39m_ \u001b[38;5;241m=\u001b[39m prefix_errors(in_specs, args)\n\u001b[0;32m--> 171\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshard_map in_specs\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 172\u001b[0m dyn_argnums, in_specs_flat \u001b[38;5;241m=\u001b[39m unzip2((i, s) \u001b[38;5;28;01mfor\u001b[39;00m i, s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(in_specs_flat)\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 174\u001b[0m fun, args_flat \u001b[38;5;241m=\u001b[39m argnums_partial(fun, dyn_argnums, args_flat, \u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "\u001b[0;31mValueError\u001b[0m: pytree structure error: different types at key path\n shard_map in_specs\nAt that key path, the prefix pytree shard_map in_specs has a subtree of type\n \nbut at the same key path the full pytree has a subtree of different type\n ." - ] - } - ], + "outputs": [], "source": [ "#inputdata = pipe.prepare_data()\n", "#shard_rubixdata = pipe.run_sharded(inputdata, shard_size=1000)" @@ -400,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -421,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -434,10 +344,10 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 6, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -471,132 +381,16 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Array([324, 310, 215, 232, 75, 100, 82, 349, 321, 575, 250, 374, 349,\n", - 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"version": "3.11.9" -======= "version": "3.12.8" ->>>>>>> 2bd5aee (get rid of reshape data, pmap and the extra dimension in the arrays) } }, "nbformat": 4, diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 76172aaa..16130811 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -74,44 +74,31 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: age = jnp.atleast_1d(age_data) metallicity = jnp.atleast_1d(metallicity_data) - """ - spectra1 = lookup_interpolation( - # rubixdata.stars.metallicity, rubixdata.stars.age - metallicity[0][:250000], - age[0][:250000], - ) # * inputs["mass"] - spectra2 = lookup_interpolation( - # rubixdata.stars.metallicity, rubixdata.stars.age - metallicity[0][250000:500000], - age[0][250000:500000], - ) - spectra3 = lookup_interpolation( - # rubixdata.stars.metallicity, rubixdata.stars.age - metallicity[0][500000:750000], - age[0][500000:750000], - ) - spectra = jnp.concatenate([spectra1, spectra2, spectra3], axis=0) - """ # Define the chunk size (number of particles per chunk) - chunk_size = 250000 - total_length = metallicity.shape[ - 0 - ] # assuming metallicity[0] is your 1D array of particles + #chunk_size = 250000 + #total_length = metallicity.shape[ + # 0 + #] # assuming metallicity[0] is your 1D array of particles # List to hold the spectra chunks - spectra_chunks = [] + #spectra_chunks = [] # Loop over the data in chunks - for start in range(0, total_length, chunk_size): - end = min(start + chunk_size, total_length) - current_chunk = lookup_interpolation( - metallicity[start:end], - age[start:end], - ) - spectra_chunks.append(current_chunk) + #for start in range(0, total_length, chunk_size): + # end = min(start + chunk_size, total_length) + # current_chunk = lookup_interpolation( + # metallicity[start:end], + # age[start:end], + # ) + # spectra_chunks.append(current_chunk) # Concatenate all the chunks along axis 0 - spectra = jnp.concatenate(spectra_chunks, axis=0) + #spectra = jnp.concatenate(spectra_chunks, axis=0) + # Single, batched lookup over all stars: + spectra = lookup_interpolation( + metallicity, + age, + ) logger.debug(f"Calculation Finished! Spectra shape: {spectra.shape}") spectra_jax = jnp.array(spectra) #spectra_jax = jnp.expand_dims(spectra_jax, axis=0) From 54a71ece086de28258beda1a542d5a1bb33e01c3 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 23 Apr 2025 13:26:53 +0200 Subject: [PATCH 06/76] sharding implementation works on one device --- ...x_pipeline_single_function_shard_map.ipynb | 198 +++++++++++++----- rubix/core/data.py | 90 ++++---- rubix/core/pipeline.py | 186 ++++++++-------- 3 files changed, 293 insertions(+), 181 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 1269d21e..2c6fd13f 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -61,9 +61,26 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-23 13:26:24,875 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-04-23 13:26:24,875 - rubix - INFO - Rubix version: 0.0.post400+gee789d5.d20250306\n", + "2025-04-23 13:26:24,875 - rubix - INFO - JAX version: 0.5.0\n", + "2025-04-23 13:26:24,875 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -220,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -229,55 +246,55 @@ "text": [ "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:55:39,695 - rubix - INFO - Getting rubix data...\n", - "2025-04-23 09:55:39,696 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-23 09:55:39,739 - rubix - INFO - Centering stars particles\n", - "2025-04-23 09:55:39,951 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", - "2025-04-23 09:55:39,952 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", - "2025-04-23 09:55:39,953 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-23 09:55:39,953 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-23 09:55:39,953 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-23 09:55:39,954 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-23 13:26:25,177 - rubix - INFO - Getting rubix data...\n", + "2025-04-23 13:26:25,178 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-23 13:26:25,237 - rubix - INFO - Centering stars particles\n", + "2025-04-23 13:26:25,720 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", + "2025-04-23 13:26:25,722 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", + "2025-04-23 13:26:25,722 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-23 13:26:25,722 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-23 13:26:25,723 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-23 13:26:25,724 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:55:39,961 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 13:26:25,731 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:55:39,969 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-23 13:26:25,865 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:55:39,976 - rubix - INFO - Getting cosmology...\n", - "2025-04-23 09:55:40,005 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-23 09:55:40,019 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 13:26:25,871 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 13:26:25,893 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 13:26:25,907 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:55:40,047 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-23 13:26:25,942 - rubix - DEBUG - SSP Wave: (5994,)\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:55:40,056 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 13:26:25,949 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 09:55:40,072 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-23 09:55:40,072 - rubix - INFO - Compiling the expressions...\n", - "2025-04-23 09:55:40,072 - rubix - INFO - Running the pipeline on the input data...\n", - "2025-04-23 09:55:40,079 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-23 09:55:40,112 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-23 09:55:40,113 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-23 09:55:40,114 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-23 09:55:40,114 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-23 09:55:40,115 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-23 09:55:40,116 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-23 09:55:40,117 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-23 09:55:40,117 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-23 09:55:40,117 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-23 09:55:40,119 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-23 09:55:40,120 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-23 09:55:40,120 - rubix - INFO - Convolving with PSF...\n", - "2025-04-23 09:55:40,122 - rubix - INFO - Convolving with LSF...\n", - "2025-04-23 09:55:40,132 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", - "2025-04-23 09:55:41,965 - rubix - INFO - Pipeline run completed in 2.01 seconds.\n" + "2025-04-23 13:26:26,099 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-23 13:26:26,100 - rubix - INFO - Compiling the expressions...\n", + "2025-04-23 13:26:26,100 - rubix - INFO - Running the pipeline on the input data...\n", + "2025-04-23 13:26:26,101 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-23 13:26:26,139 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-23 13:26:26,141 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-23 13:26:26,148 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-23 13:26:26,149 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-23 13:26:26,236 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-23 13:26:26,236 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-23 13:26:26,238 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-23 13:26:26,238 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-23 13:26:26,238 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-23 13:26:26,330 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-23 13:26:26,331 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-23 13:26:26,331 - rubix - INFO - Convolving with PSF...\n", + "2025-04-23 13:26:26,332 - rubix - INFO - Convolving with LSF...\n", + "2025-04-23 13:26:26,335 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "2025-04-23 13:26:28,030 - rubix - INFO - Pipeline run completed in 2.31 seconds.\n" ] } ], @@ -291,12 +308,87 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-23 13:26:28,042 - rubix - INFO - Getting rubix data...\n", + "2025-04-23 13:26:28,044 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-23 13:26:28,065 - rubix - INFO - Centering stars particles\n", + "2025-04-23 13:26:28,273 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", + "2025-04-23 13:26:28,275 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", + "2025-04-23 13:26:28,275 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-23 13:26:28,275 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-23 13:26:28,276 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-23 13:26:28,277 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 13:26:28,286 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 13:26:28,294 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 13:26:28,301 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 13:26:28,316 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 13:26:28,329 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 13:26:28,349 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 13:26:28,358 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-23 13:26:28,372 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-23 13:26:28,373 - rubix - INFO - Compiling the expressions...\n", + "2025-04-23 13:26:28,385 - rubix - INFO - Number of devices: 1\n", + "2025-04-23 13:26:28,386 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-23 13:26:28,429 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-23 13:26:28,431 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-23 13:26:28,438 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-23 13:26:28,438 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-23 13:26:28,488 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-23 13:26:28,488 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-23 13:26:28,490 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-23 13:26:28,490 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-23 13:26:28,490 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-23 13:26:28,516 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-23 13:26:28,517 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-23 13:26:28,517 - rubix - INFO - Convolving with PSF...\n", + "2025-04-23 13:26:28,518 - rubix - INFO - Convolving with LSF...\n", + "2025-04-23 13:26:28,520 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n" + ] + } + ], + "source": [ + "inputdata = pipe.prepare_data()\n", + "shard_rubixdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(25, 25, 3721)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#inputdata = pipe.prepare_data()\n", - "#shard_rubixdata = pipe.run_sharded(inputdata, shard_size=1000)" + "shard_rubixdata.shape" ] }, { @@ -310,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -331,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -344,16 +436,16 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 15, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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OvNmtBxVZDv6JPzQGYHfNgsgBpxZVKy15YSJVv/oHxXsetVQbI2hZX2QvbVu3lKqe+xXV79/DrRZmLRcdpSZD8SWR0GtyF9oJV3PZKAXA8m/+TW2tLbqWj0iwgNTQLrt6VD91Ay1/9CTNxoRTlt1LWZKHp3n78bqNNzEEAIB4APERI6Kw8Pcl8bPrzVtVv+PRKKCln16qNRlHnqQr1v+ZFwjz4zaR5KRIf9UQQCwzpcer06hy54u0cdZ1tPRPZ9Dm/x3PJ+KoibI6q57FJFR8aC4bpfgYM/caWvj6H8glmRMfbtlBy457nj93ym1U9dwt5Hqkly+OKAKs0NyY5oW05oePwj7T2v9idVoAAEgGEB8xoheL4XL77+aVk17b9sXM9h2huZmx7bAARiOId8YeEz+7WDpdy/jCREmO5HNV9D6yksY3/kADvVto43Jzbe1FOs+5O6rv6Qk41huGiSlW80Jvf0sxVDjtu+Vt099hosjpyubPnbKHKnfOIqckU9MnvzUcXNvWFN5/5vDBuoh9eQAAICOKjNkPPcuDb+IOdXJU1L6qsbiO+NAw5Q9pXGoodYZVt/TjjtRxVxyS10PO9sNE01IgqBLR5eAUap2YhQVmRkOkGhrL/nQ6t1DtvPwH7RThGHrisMZxpr8jyeRoFx85ctDdMqh5te73mhobKL/9ueQIP5UP7d1BxSrf87ph+QAAJBdYPmJFJxbDb63Qq/sgoqz/EJrtor4ORX0KHdyCqZ3JCSu6vQbeF8bmouDykkZAZyis6JVVyBGyRfyusW2zn9b8eyQDbhetmJZcf5U5kziyfOKjl7zX8G/bdDSYveNwucIsXYP+E8weMlrGHwAAEgHER4zoNhEL1PQwGDypMukFgzdjyyjxuNto+4YVVPO/U6gzKVNOjU6y2tVDg2PLEiZ/Z8iEqMW+XVvIKmSPMctD5e6XKXveP1U/K9/3TsSAWbdGf5Z8Wb0bbySisRKJKbOhAbo1C77U/J6nLTm9ZQAAwA/cLjGjZ/kwV1BMdGv0XP8aLfm8D+UtmEluR05UdT5C74Qb37iGhrlrTH1PYfnQGIM4UWcJlg+HiitA9fsxuDnCx2J8Yh3RquzQLDL/yWupsH4t9bjubeqisT+zssO7MWdL0QWrtkVR+KtViPMIrZrafGiP5vdkuF0AAEkG4iNG9EWBHPDpG0IQH/28O6hf1S8Drzc4B8Vo+WilfE94UGLEIRmwfIjFx/yBp0ap3baeDu3eQsH6oLHhNWj5iET5vv/wx3lv3hPoSCuya+MKcmXnUj9LtkaU37mH6e8c/jAYkOpuPEgLP3yGhv74fCrsVEyeRu0S6nq9awAAIBFAfMSIvkvFpKtEJ1NDu8iY0VWzHBfz9T12bVhKu2SZyiZP084k0XBRROp2y/qhlPzfJCoha2iTnSRbHM+Q3aSeMTLondMs3U6/sgmmvyOWeZ+y7D7+uGzV2zT2ri/J21yv+T0ZdT4AAEkGMR9xRLKwRgVLwYyFaCuGln10LpV9fB7tq92m3WlWIxjWG6FQWP0BbddA2LoMFEZrpSzytkYXc6G5XUf0GTtmidW6JQoSuVnbyuUvMlb9yv20+Q9j6MDenTFvFwAAzADxEUe3i1G7x5qskREtH/7Kl7FYPmS1fjIGqa/bpZ3totXzJUKPFFdWeMyEFm4DhypL85WFBmpW4HUaH2OseKRwoaNWuXTLmoVU/bLP0qGFQ8/y0W4dqtjwNxrg3Urr39JfFwAAWA3cLjFiOJNFg/mdTidXWwNRWwTxIQRyRoN+G/fIsMwVrQwQzcqnEcSHmZ4wvvRgT8SeKnKrsbLkWhygQurib/7Hxuj0pcAmAo+KlaW1pYlc7Wm4jI0rqnkKbf8I63K26sT3hMR8uJr3mx8sAADEACwfcbR8MFdJ/cF9ut/3ZuWzghjtL7QFgitGy0ckIWDEcqIZw6FTdl1/pcbjWIzUJuGWD3dsNUN25gxWvJYT6HbxSOH3Aq3NQUvOvDf/qFm7I5TcFvVYFQazDi354pXAa8nCbCMAADACxEfMaN+9l7WtpqInIvjxJYnkdvExdsdrmos5I9z1G7F8yFEEnIrf1xILWhaMSNYWr4n0YY9foOnAe6q0xSY+PI4QS0cMKc7rnUohEwlv6LZ5f5ZmbnFav+RbKl/zsOF1ZXu0LUDDtr5G43+4SSE+mLhcM+/zQOl5AACIJxAfMSLFvAIHye3lzvUqWor1M5Jh+WDf165wai4LJvix8TEZrcrqaFNv2hetAHBEaUnZLvWiIfctonkj74spuLWtpZkWffQsDXn/TFPbZw3qGFsdpWGfiW4lv+twwX/+TMM/vYA2zjzX1HYAACAaID5iJNbiX/zSb+SuPtaAUz7Rx2b50Kz6qZntoi8utCumRi8+pBhTbUMFgMMbXUEuf3CvVol51sk20rb9lo/ipU+a3n6W7NsPe0ddF3FZVnK+fPVD/PmY5kWmtwUAAGaB+IiZ2AJOffEekX8GFkwZC/WrZsc0Um75MJlqG6lEeaTPRTwGY6ON9GXRIzTANK/ZeDqwiNf/m2qIDzUxpSY+3K3NlC2bt7642i0fzg6Fpr8LAADxBuIjydkuDCOWD7FbbDRUbn/eAsuHltslurLpZlxBXgP7iCHFaiEKER/D2/Q7y2ohB04t9X3OKsEum/oMz65ZftwLvu+oxHx42popq11ImCG73fLhgvgAAKQgSLWNkVjrb7B4D0+HLkQR4vyypORmJLBMF22x4I0y1daE+DCok2O1fJBF2S1+sSTp1FYZe+LPSD7+AuricGim9TLLh9gp2ChZPHebKDsP4gMAkHrA8hEjHT0xZgdIRFJOEaU6vFuslliI0u0SqQKqYl0GrTaOWMWgRUXFgpYPfaR24cG/oyJ8PK0tUVo+fN/JyYf4AACkHhAfMRJrFgrPdtGIC4gGvTLksaTaMuGhWWRM8/1Ibhfj+87bnhEUCZfbfHdYEauKihlxpYVvO1x8eN1Npo4xlmXDfo/sdstHbkEn0+MAAIB4A/ERM7HGfEiaGRHRsOuy72hlzjiyGjahaQWcmn0/uE7r3S6jW5ZQTFgkPoLjNS74JBWri7ethbJNiI+jziJyu9vI2d5JuUN+6lvVAAD2A+IjyQGnErtDtlB8uLJzdKwE0Vs+mJDQEgueNvV0VLm9yFjttvVU9dI9VL9/T9RuF6MBpzHjstrtIsdk+fC4W8nRLiSMkCW38JLsfnI65Bv+LgAAJAqIjySLD2aelxzWxf2yGAJJKwYjhvVyK4aGe8Wr0U/FX/nUPesnVLl5Jm148YaoLR9GYyhiYV7x2SRZJT4iiKWFhdPC3pNUrC7yutmmtjvIs4nWPntl4HV2TgdakTPB1DoAACDeQHwkO9WWZUNYaPlwWihkQq0Ymqm2WiXN2wNU+8i1/HFIwzzlxyYKtEUTQ2GW0Vf9naSsDpasK9J43Tmdw990hYuPyfWfm972xIZv+GOb7OQNAQvP+bOp7zc3xRY3AwAAkYD4UOFg3W7D1Te1rAyGsdjyQTx7QsPyoZP2GQlmxdASC14N8REqVkKrwcYj5iMWHE4XObJyLVlXpDofaoJTzfIRC23tmfQOl7n04SX/usvScQAAQCgQHyGsnfcFdZ5ZRkv/MkP1832122nezGto8+oF1vR20SnBHQ3ZuXnkmHqH6mdHs4qjX7Hs0Swlz7qkqr4fsrwUQ28Xf/+beOJwOMmRbZH4iCj0wj+3yuXjp0XyiRmnSiyJHr1qfZaTRLJnx0Za/uiJtOzrtxK+bQBA4oH4CKFp7t/444Sj/1X9fNeLV1J53b9pwFvTLAo4dbJbU7KKjoWdaeSxZ1D9zesMle82iuyVddwu6uKDdKxH6xbPoYYda1LL8uFwkDM7z5J1RUoNVkuvlgS3C4s/iZU28v3eDpcrqr4wflin252bjP9W0bD7tZt4X5mx30buRQMASH9Q4TQMfTHRu3m9xQGn1lo+/EWriop7kJXIsls7RkOz86v6vlm78Csq+8hc99RExHwwy4fTIstHRF2v8vc4BPHhze9OtD+2EbRJPvHhMmn5YOKDWfi2vvILyqm8lvK+upcGerfQ1gu/pn7DJ1I8KGzeHZf1AgBSE4gPk11qQwt1xR5w6iTJGf+fYaNzYGwrYJkuGvtG0hAfWsXHDq38yvTmEyE+WCl0p1UxH4HxahwfKpYR0e0idVAJSDWJu93tYtbywcq5b3ztFpp0ZA7R7DmB93f98HrcxIeDkts+AACQWOB2CUGK0MAt1Pwfs9uFz0Pmxceqk18ztbxHcml3gDMCT7X1mBMfWkIuCkuP0QqnsVqNug8YZcm6AuPV2meqlo8sS+M/2vwxH4JFpZ4i1/3Ikt1U0Lgz7H2rMoHUcJro8wMASH8gPkxmr4RbPmKE1eWIIuZjeMWpYe8tyTtW8Xpx/lTDFp1YKpw6TLpdKAqxlYg6H4yuJaW07/rlVFUaa+yB78jQjJNRi/kQ3nNk6YsPz737qfHX22jHpd9rL9PudmHptn5W97kw4shZaXY1sSfpxMOsrv4sEIRtht1ba6j294OpVN5l+rsAgPQF4iOMSG4XhyJoMtTy0SjnUFXptSa2J0Vl+XA4g5NDLXWjPdcupjG3faBYZvSv/k3Vg2+1JjaFCQ8N8TG5/jPV94cse5zmv/sPlcGbD3xNRLaLn669+lHx+J/EtA7/eDXTidUsH8JxIOmID1Y0jAmKvIIi6jNY21LjdvgsHi6TqbbZklu1W7PsUQai+qndvoFGfHZhIAjbDLvfvoNKqE7x3uFDMQa7AABSHoiPECJZCLxCCuXQD2aETerMMiJl55us8xHrxCpTjz6DFHe4jKzsHMrr7Zuc2DjNCJCwBnVsEjWRGsvoTIdpyrJ7w96P5u9Vi/lYMO5hihdqbhGTK/A9argTeJZT6HuC+BBdJSJVfa6mIb/60NAQPO3iwymID0dB94jfY64Zp0onXa2spv3bagyNZ93iufyfiNMdXh238G8Dqf6AUpAAADILiI8QIk3QkQJO+edmJi6HS3HHazXBlu2yKdeLJ+TQyF3/kXZKrelBSZZYPnKKghk961xDyUrEVveRUNt20PIR3OcLJzwqfB6+DxyC+03L7ZI/+EeUm1dgSnyIlo9+x/6U5nU5S/d7RXRUPQZDq5Kt8Le429StI22tLTT0g7P4v7XzZ5PH7bOsqIkcxpalia81AgBIHBAfprNd9ANOuWXEhPhw7Flhaaqt1sTg4LEsxsVHaGDtmOYFVLn16ZiGEpPjR2WyFgWCl6zeh5K5YF4NS02nwVMC7zmygwGbar+5KEIdLvXgTjNCNcvTGNhP88c+SNXD7qSS0sHU5yeRK5i6Qmp98PVoxvYEaWlW7/PTeLQh8Lzsk/NpwfO/5M8dGpYhM9VvAQDpB1JtDU6RNQu/Jtent9MgWVmPQGVKNHVn73IfJYfJOgzhW5QNuA9kzQu9Gl5Lardah2rMh7CfrU7F1ROECyf8kTxbf6Dy/e/z114d8VE2eRotP/ICdS4dRrRxqbgB3WwXZ7ZGzIeZY8sT7DY85ZxfCquILNRy5XArlySsT4EQpL3m27epQ6devNCdSJvQaZdRUfsq1e36DTm8bvVVQnwAkNFAfBikz4cXUb4UfucX2u6cT9omJkI2SbHiVnGjfbJiKcRm3C7xqCganDatcbuIhjuvxeLD4YgwRmE8auJDHNuYE87nj/s2LRM+1s920Yr5MCIchIXNvS+QQ+FCQ9IIOPV6ggJi0oJf88fWSXsoOydYM6WtJdwi0u3ZsdRNY/uyR90dAwDIDOB2CUHtssyazKkJD/XvyyYn1+iyXQyvvX2y4vVEzAScppzlI/xQlQSBYDYbZtlxz+svEEHMiNtzT7yGGuQOBsbrjJDtErR8uDQqrZqJRdESkKyMfCRyZRUrh1ZjQXe4laTpSL3itbvV2PkT3BQsHwBkMhAfBlj86QuGl2XBemYmCBagGmvAqa6oUFg+zDRyS7FDI5LbxaRYGjRRPy3Uoff3s+0KwaH5XftQh3u3hSyjL5bU3DpitpKW+HDl5MXe3M6A5SNbCj9WtI4fj4qwOHxwLy354hVepn3J5y9Rwz5zdTyKFj9pankAQHpha7cLy0TYtm4p9RwwImgiVikyJq+fbW7FJibunMlXJCTbhRVPi1RATS/bxQrkWLJdVKNrxDGaW6cz0j6P4AoTi4QxIeHKyo5YREwhoFTFSeRU25wOxjJdfGhZPqJz82mJD29buJWk9JUfUSl78gNRVyI6SIWmtjXYs5HmvfUYlV/wm6jGCgBIbVLs9jaxLP70/6jfGyfQyr//VHc5WdWnr4fxiXD01BmKgmHx6FfiGxHL0zETcJr6h4ZoYYrcwl6JWPtCdd16q+MZTYL4aBcSS46ZKSyjEtMhCg6Vz0VBJAaf+llcMJX6DhtPRtHaJ/5jwixaMUNet0YgakjNF7OUr36I1i740vT3AACpT+rPMHEkf4nP7z/h6LfWNYrjKzG3W2PNdtEdipDtYi7mI7UCTtVXGP16Ilk+dNOfmQVJLIXevi5lbQ791GA115xoAQstGMdqc0z49Ye6Lr0mOdtQPxwzbkHF97TSYrXqf1jA4W0r47ZuAEDysLX4cDtjb95l5Z1lXOt8mCwyZjaGIu6o1flQvGdS8EWYgCN9Lgn7J6vdZSe6M1R7t4iiNILrI8ztoiNo1531Pi3O/zGtPyG0DovWd6L8bTWOH9mA5SNaJA33EwAgvbF3zIfqRTjxlo/mI4cSUufDnOXDeldQYOtRNblTEx+C28Xs2iLe/esHnMpCfYrCLiXt69SP6WBNBIOr0N9+WD8WneWHTjieaMLxtHPTKuU6jrs96myXVLF8eHYsjtu6QfxZ/Oksat30PY267HEqKOyc7OGAFMLWlg/LzP+hGLy4e9r7p/QaMi4+4xAmWVbh1FSdjzhab7z14e3aI6I2HvG9kM93SZF7mOhuLpLlQxAfOR18vXwU3YlVYz6UQap6hMZ8GEklzskLBnWyzrwjKk9TH7uOkPEfk6rfk71U/dqDtOahSmqoP8Df27DsO6rc/E+KFxV1b9OWNQsDr+v376Elj53GM2hAarNh2fc0pvo2/huufOXOZA8HpBg2Fx/hSAlcy5rcsfyxY1EXmt9ZWRHSKqK1fMTL7bLw4+eocucsS9alnESV4z3qLNL83pK8YyKuO6J1QLAC+INXJSFwWC3YUyFoVOt8CEXGQoOQDVjTupaUUvWQ22ne8Ht4Z95o/ja9LCdm+ahY9yca3raaVr71AH8v772rKd7sXfUtrfj2fap+/SFq+scxNL7xBxpfFazYClKP1pZmkj64iVyS74ZnzJ730K0YKLC328Xg3b3ZINRoOqJ6OhSzfMS41flw8LJhSsvH/FH305SVvkkkUg8bK+goNQUqYFqSaitmu4R8blY8VQ+5jSoUK9f5PvtMsCL5XSSKFFYVS4VYO0TN8sE6Ey8snEZeZy5NDE2pNXhMVVzyu8gL6azLF2is0YlXOH5cR2r5Yy95D8Wb7I2f0+iV1XHfDrCOxW8+RBWezXSQOvJzsYt0mKo/fYYqLron2UMDKYKtxYeqhcJELQzt1UZjNYiPpUEUQmEiRWcSSvWA00VT/kYdxTGGfG5GPM0f/XuqOO9WU7UwJKECZ6CWiomYD1W3jMNBk277D3/u7/qqWzckDi4lvSwnsTeQJKv3ZIkH45ogPNKtflLp5jf58/Wjf827YZevfZR6rn+NZO9dUWdbgczC5kdB/Cd8PRSpkHGqKCoJlg9HSMyHXlaO2boZ8Sc4nlbZSRNPv0rX8mGm10uHbv3Dt6bz9/NMF5XgS0WcRoRsF4czQkxJ6PYtPD6id7t4Yxbp1d0viOp7IH1Yt3gO9Zb3UKOcQ6OmX0XDT72eP+/n3U6rqz9L9vBAimBz8RGOJXU+DCr7llwhKDKGyV4320WsxBmyXMqVUDcISxuOKJ4MHNoLJ/+JqnpfRaN+NCPsM727s95jjlfN2FG6XSL1dnGaFB8WWj509pueaBPFBzuW9u3aan7jjvjVtAGpwcF5r/HH1UU/pryCIirsVEyrOp3A3zu8EuID+EjP2cciDMsMs3d5BoXEwIv+lADLB2nGfEgWuF2q+l5PiSYoosQxSpouAi0mnXEdVV73N1WhobVvqksuoZLSwaqZQ4pKtariw3iqrdHxRIOeS0kvxdoltyrcLnnPTDa9bTmOBfVA8mHuwsF1vnYUWeOClaO9vSfyx7wDq5M2NpBa2Fp8qIsEOayjravtsOUTxcKJj1Fxjz7xFx8O7a62+r5XY+Ijt48vYyfuKGJX/G+Jk71yvE5yx/S3aVkHpE68Y4mG5cNl2O0iRXK7hPw2VrrB9Cwfem6Xoe51gecTjnxLeVIUxcWcKBqWyaz+4WPqSofoEBXQ8GPPDrxfNGACf+zdvD6JowOphL3Fh4FJaM0fj+epfVbvVkd2aHfSOLld2tfLemv0kvca3qZRW0+kehXxwCGFu11C3SxuSXmHvTprlLltaP1d/l45KpYVMdVWzU2SV1gcHK9p756F4kMQNrukHont6ZMAy8eyb96mRX+awWuCgMTSvMjncqkpPinYrJM1GiybxB+ZMDlYtztp4wOpg83FR+SJfGTrcvPrEFqnazHq+JBmdvEK8NQTB/rd01JWfAS3LWaPKD9rmvhzWucaGnjdWnGzsKwUw9/lFx/6lg81q1IXwdLlNVsV1FLLR3Bs+49/lOZ1PTdhWU6ShZYPllWhxti519LEI3No7et3WbYtEBkmKsYc+oo/Lyq/TPFZfsdOAaG7c12waBywL7YWH3ELuNQIDhQbf4l3Bb7vhF/0F4x7mDY6B0benN5nUX4op5z4iNTbRUlOYXcaeu8CYVmTf5vWuv3vq8Z8iBVOw4+tnNw83Tb0oXjFaqMJynaJR30XBRaKD49HP90392gUlXRB1Kz95B+UI7XReudgGjbppLDP93QYxB+PbDN/QwcyD3uLj7il2qqvd1mPc3S+pBKg6MoyVBLdoVEUyrfaOFs+EpWSq1YxVBxjiBvDX3XU+sZz2m4XZcCpeh0PbbdbOBbkXUW0fLhyfaXh/ZRQXZy2an2jOHdbMABWjXTN5koX2P6veukeWvTx89TW2kIDN7/B3z80+mpVy19z52H80VG3JuFjBakHzs44pNpqBZxqNebyb1ltPW5HtiUxH2Y/M0wy64EohJVyHzjaJ7nF+VNpm6M3lR3zE3OrVrl4HqY8Kpt2JX+uJgoVgkfjGFiaV0k7pR40/EfBYDxD4tjC/Sz+bXmFXSmRWCk+2kLEx/49O6h65rWB114D5w6InsUfPEmVm2fSxAW304GHh1MP2k/7qYjGnHqV6vLZvXxxV0WHEXQKbF/hNE5oTRQ6VgxVwSJJ1OrMI93EDTbp6a03SsuH8cZy1ulXFnuQ1byPZ1IwaqkrldA+7fLq4twcWsOkvQLphDs+5BlLCqtEFNku26VeVHL3UsrKzmlfg1e/porGfh/760/I6/WS02Xu1JMs3M/srrS6x0XkaKmnycPGU63PTZ8QHFa6XdrFB0vvXPvYiTSydRkFQ3qJ2nI6WbYtEE7hWl8VUwYTHoz1A6+gCsG9KNJ14HiiBUR92raonJPAbtjc8hGf8uqaAad6LhTVyd5BXinyJMVqeGivNto7ZqNuF7KMMVf9XbHdSHf+ir8t5HdztwYDOsMucpL5bJf9x9wXEB78c2+4yd8pxnzoTPxGhYf49+s0m42Kihufpim3vJ7wUteSK7gPY8Xt9v0G22oWceERipwV0h8HWMa2dUuprG01uWUHVfW8lL+3l7rQqLNv0/xOr0GjeHXifKmZarfB+mF37C0+4uYyiMLtohbzwap4G5gpnXriQzfzxoLeLhbuww75viZUgTEo1q0mPrTdLl53m86WjGS7BPcNsxKMO/lixefZ7qP6AacWEFKVxdJ1KzcUOa7IKhQl6C2K+VDUVxHxJq7/jN3YOdfXmXpV3mSq/PlM2nT+F5RzczUVFHbW/A4T7zucvjo5ezcsTthYQWpia/GhasqPY8yHacsHe89A0Jy+5UOnXLaUggJOUbtDf92isAr93fqN+VGMwxDTeMP3YWulr6X7wo4naVhYLA4XzZDgSUeWdZYPT5tPYLa1Nql+Lnn1BCiIFuYyGbDzI/7cPfoi/jhwVDkVFStrxqhxoGAwf2zeuTLOowSpjs1jPuKU7aJhyvbq+aBVYz6chiL2XZKe5cMRt1Tb9TM+JKmxgazFuNtF1M7yuEuJfqgKvGb9JGL528SgTLXqomNP/BnVlVXQhB6lgtvF6kaBorhKXfZRJ148yghOCwNOPe6WMBebCMSH9aJjwX/+TF1q3qQhtI8HYI88wVyjwLauI4gOf0lZ+5HxYndMXSGfeuopGjNmDBUWFvJ/lZWV9OmnnwY+P/7447kfXvx3ww03kO3QmNSHX/AALc+dTAvGP2JwNex+PrZpx8qeIKF07tHP8pgBhdtFcXiqWKmEbXcbOI4WFJ1ibBsGmrQp95v639itV3+FtSNSnQ+zxCvbxWoc//MDLT32qYRYPnZIJXSACvlzT7trza1l+fCkl/g42nCIjhw+SKnKgnf+RuWrH6Ihng389aqB11JuB2WqdiTy+ozmj8VHfesA9sWU5aNPnz706KOP0pAhQ0iWZXrppZdoxowZtGTJEho5ciRf5rrrrqM//OEPge/k5UWuZ5A0IlzPq176LVVGs1qNiaeoSzcac9eXlEgcum4XnR1gZLJjbo84ul3MLMomfk9uF6J6I1804MpSVE81NiYx4NRy0ZfC4sOVnUvZBdq+fistH6yjsbv9suXPdvG2qhdsY83v0gWWsZP7p/7klGRqu2evIrg5VcjZ7GsYt6jjiVR8+m+pcrivZLoZegweT/QtUW/PTmptaQ4vtghsg6kr5Jlnnkmnn346Fx9Dhw6lhx56iAoKCqi6ulohNkpKSgL/mIUkddGP+ajc/M+kThS+CSy4rl1Sd/Mr0Qk41atmaczi4rNuWYtWwKnKkkJGCrdAGMxUkrUCFBXrjk18WHEMJCzgNEaYu6lsijGrkzNLXXzM72y0DotMnvbOu952y4ZHo1S9I43cLgvf+SsXHoz6fbWUarBS9n0bV/Hn+cf9gvpHITwYPfoMonrKpyzJQ1tXz7d4lCCdiPr2zOPx0BtvvEFHjx7l7hc/r776KnXt2pVGjRpFd999NzU2Nuqup6WlhQ4fPqz4l/4VTi3KX+cBp8ExeqII0dEPOI0t5oO71izeh6LgaJVyI2QDSSFiwaD4MGuVMLi82VoimeJ2YaLLaM0Gh0ZjuYEXGnNFOmSZPO3p5962Vj4p9v7hPs2YD2ZR2Lx6AY9XmDfzGpr3T/UCWMmmfPX/Bp57UjBLp273VupCh3lqbf8R5VGvh52nW3JH8Of7135n4QhBumF6NluxYgUXG83Nzdzq8e6779KIEb6D6eKLL6Z+/fpRr169aPny5XTnnXdSTU0NvfPOO5rre+SRR+iBBx6gpBByQW862kDZ3ubYV6sSB7HeNYSG6H1J5a6dZXMw95afuvzBVHpkl7mxxHHS4uuOo+WjhRVY08tOFkSBk1kzjFo+TIrDqASW1fUzUjjbJVLcz0Eq5F2VGVoixaXijtlN3ahnSLn3Pbn9qXPrLq4zj+7fTqu+/5BG0QHVdbJu1POevp7K9/2HFhSdSuX1n/H36w88xl2gqUprs/4NWzLYXTOPmN11h7MP9c+LrX5KY4+JRFsXUNauYO8lYD9MX9GGDRtGS5cupXnz5tGNN95IV1xxBa1evZp/dv3119P06dNp9OjRdMkll9DLL7/MxcnGjRs118esI/X19YF/27dvp8QRnFTWzp9NHR7vQ6XyLgtWGz5ZRdeqXPmdAVc8S/O6nkcbz/3U+BqibPxmpMEYn/zjKD5aXR2D44lQZEzRzj4C3k79TY0okvtHDcmAayfidhWiJ7UtH3q0UlbEWiih9T82OAfRltIZgddH5A7cNdP78heor9fXMG7i/Nvo6O51uttmwoMxuV14MBob1MVKquBuST3x0bhtKX/cVxDsFB0tHYccyx97N6yIeV0gfTE9I2ZnZ9PgwYNp4sSJ3GoxduxYeuKJJ1SXLS/3mec2bNCObM7JyQlkz/j/JQrx4l72yflxDfI82He6+RWFWBY6d+tJ5b/4Pxo05hj+emHhNP44v9PpOqvQ6e0S4910XCwfwvrcigqVjogTYKQaLcuP/z9ewn38T022Wo9iPzlzLA60TmG3SySB2yYJ4sPhoqp+ygy4FjmLOgh30+x1wZVvKfb7ltxhNOVXr1LXXv2U244igHXHki95IzQ9mDsnWZknzEWUauTs88V7uLv7+rPEwoCxU8kjS7yJ4Z4d2jemILOJ2ZbL+lSwuA01mIWE0bNnT0pJ4nVBD1nv4oonaNJF95lfTYS73VE3vMQn1DE/f157oWi72krRx3wsyfOJo1gFodcZjPnI6TshbFlve/8WPhYDFp4xx59H5b+YZTo9MBqrQ+fS4aa/Y/UYjBNbQbRI8R5uUXwwC4fwWzX+ehvJd25RWE+Wjb6HSkoHK8SHV8NVFk259slL76EjD/vau2ux8O8XU+6fB9KWNQsp0YjHdarQ/aivHHq+ynlolvyOnWiLawB/vmO5r48TsB+mxAdzkXz77be0ZcsWHvvBXs+ZM4e7WJhr5cEHH6RFixbxzz/44AO6/PLLaerUqbw2iK0QfOBeWaIJp15JLo0of11YY7kO2hkuuXkFfELVm0yjTaeVY/D3643ZFJJEWy78iuaP/V8aP/2KsI/FeBgz2S7RjMMoq6e/QQsnPU4DRkYflKcmxOIZu2MV88rULUqNziLF7zTizFt5Z1/WEySvoIgfx4q/z/9cUe3WYWndkM7UQC06sRWTD33Ki/ft+fIflGj0WwMkHmYB6iPv5s97l0WX5RLKvs7j+GPblmBhQGAvTDmm9+7dywXF7t27qaioiIuKzz//nE4++WQeq/Hll1/S3/72N54BU1paSueddx7de++9ZDdEM7Tx6VAt4NRBZRc9SkteqCUa+zMaH9VYjJVXX3Hii+RpaaJx39/o/zTyykNSgYPvx5DtE9LPhaX0aab1ieLDRLZLbGPSZ0TlaZZtNl2yXfyU/+xu2rp2OvV74wT+uqr3VdSx7ARqq3qaqH0+dTmzeLBn4X1rqbdwbCpFrBTWyVcrSNgRQ6O6Q/t289TPUBqP1JPfaebNj1wy3Gr8HZlThR01i6isvXFc9+69LVmns2850b53qPMBn3Uc2A9T4uOFF17Q/IyJjblz51J6Ea8LuvH+JPo4eL+E8b/5NPqRGJy0OhR1oyP7dkQ0c4esXHX9cpRBru0rNeXyS0hWT0q0QIpnYznrRJsru0PgeccRJ9GoY8+kJdXPhgUG62XI+H9LMdDXdHq0Adyt4Z2JF7z3T+qy4nnySxJHQdfAZyv/+z63Sow54XzVGBGPx02LZ15BnRvWkfOcJ2nAiMmBv3PTynlU982TlD/hfDq6YzUNn36tZgsArye1Um2P1vmSAPZn9eQZL1bQa/TxRIuJBrRt8Im9gqB1DNgDW/d2iSaLwQjihTUW8WHF8PSDAYU7T8lBXkWxJmMxH+oN8SyyfESYcBQt7i3uKCtidTt7w9sVX6SB5YPhys4Oq+khukxcBrra+s9LMSBay/Lh9YQLCKPsWPQx9R6ojM2ZvPS3yrG0nxOsGuewL6/ixbF2DxxNPfsNUyy36K/n08CGBTSlPaWY/n0KVQ/6FXUcWE5DJp1EjneupXLvNqLZ7/k+X/MwNdy6mToWdQn/m1KszkfbIV92UWOOdenJPfsN9aVSS3W0uvoTGjfN16AuU2EdmA/W7eItGYCPVLilyzjEu/CY7iktmHAMx3ww8aHRoEtv3VZbHMzc4bITubrkEqoqbe8xEa/W8FYVjYtJHKeJ+MjSF4ShKbXqtP+tiiqzGs0aNfq67KfId9Llqx6MPJR2IdB0pJ4LD0bDfl/8g8ikhq94ES6Rio1P0MjZF9OS526k/kx4hLDqtXtUNyknwfKxbd1SWvbN2+rjadjDH9vyult6g7at64/585bVn1Ams692G219dAp1e3YsrVs8J9nDSRlsLj6sv6DXUjfr+npYIT70zNvC3+9wSDTkx+dTo5xDK3PGGRwe+75K/Y2Yylqbm3ArbniSKq/5cwzbMzeixJJeMR/+Pi/hgaOk3vlXg6CgFTKf2quahuJpOar+fnsJdiNprdWvP0xVz/6Su0604i/E4NShH8ygg3XhAkSL8n3qRRbz6n0ZJNQeTxHYZhJSbfu+dhyNnXstbVwRbJXhx9m0z/ckz9rCbB1GnsEfBxz4TnXfZwL1B+qo4dkzaZBnM3+9f9G7yR5SygDxYSFL8o4l1w3fhKR9xuJ2if2O27AQkhxU1LkrOe/eQiPv/CY2y4dVPTVSZcKNKYbFKpK3L1gjMaNkiw3RArEkwbE7zVg+xGNX4ziWW9XFh1tDrIQy7+V7qKLmj1S56yXasOw7TctHS9MRxds1s4Pp7cwlEw2NnYKuG6dQyjfRbpfmpuA+rN8VFER+HN5215YQz2MFQytO4zc73ekAbVyRmVkv61+8gQZ4twRe59T7RAiwu/iweHLzDJ9BXUtKFas1HPOhVl7dgvHpZbuIA/VvKyc3L2K5bMV3VCYFh+CHZ37tDed8bHzAop8/RVwN+X3HJmW74hER34Badefg/FH304oTZtGw6/7P8LoipZRHqobqG1C4+NAsid+mXmPIaB+kyq1PB54f3LQofIF2y0dbaFquMLbGhkMUDZK7mVtUWMEzB3mT5nbZs7Um8DyrQ3jpdIfHt48ll7UdaJmrtCZ/In9et/gDyjSYi2XS4S95uYWqXr5SAQXNxi1mmY6txUfcGssJd8o5UgxWAEvEh/Zdu6I8eRSuIvYd5q7RulgxWEBdpx59Ta+7fQMmv2Btqu3SHz1N1YNvoVE/Cpb5TiTKVNvEn6oTz/4ljT7uXMrLN151WBFQ2i6oxb/DiPiQVMWHQyGKAu8Lx5pWYbP5nX3m/UhMWfH78De9btq/Zwc11u9VjtEZFFksWyMaHO5GOvroMDr4cBm55KDgkBNs+Whoz2ZhsHT7UJzedvGRZa34YLQN8nVD7rLTmLU1XWBuJPdnvjITizqdQt3KL+DPu7iVx5GdsbX4iBdWdXqVEhxwGtW61SwffjNtcEkza43ye9amjDJYBH7FpQ8YtgTFk2QUGXO6XIFtb3YYi9IX91XXPuF9QIztSyn8bxZE9JTzb6Nm2ScupDb1gFMxVVzq5+slEg1lO/9NxU+NpFGzLw3rQjv/iYv587YQl4xROjTt5kGqzO3QUWpKWp0PsZeMR2V/OtvPZ4cYz2MRAyrP4Y9D2tZxkZcprPrhIxrRuoK3Cig9/2Hq2nswf78rHVK4uexM8q+qySRefUksihGwInBV/2IvptpGkYasFfMRknViauJUq3Rp9KvxKjKWJESLQbzSwo0eQ33vWUTV3X5qaPmtP/uGan7yblgfFuMbjOx2aZF8lgdJw/LhEI5ByVCcCVE95atWQtViysGPefXP1igbwUkaYjnRbhd3S1A8edZ9SesWzyX6fRHtfGAodwu52sWHM8vamA9/xhprIuiQZNpU1Z6GnAF4vv8nf1za7SzeKqCoS3c6KvvEW90O7V5ndsLe4iNObhc1V0RkVC5ECZxwjPRGMdrbxTnlWh5IxipcZmon18SQmH1hxP3IrSAGO/X2K5tAwyadGP1xrCI+QtOdA9ksGnU+JCGA02jzuRYyXy11+5r51NYc3Z2s36IQSqItH7IgnqYc+oR6fOCrudFb3kPLPn2eXO1uF2cc3C6MfT2P54+OjV9RJsAyqAY2LufPi398TUDA1zl92UKHdgcDUO2MrYuMxa+xXOqk2hpdvVFX0TrXUBrqXhe0zKiMsdfQSZR1zHaqbM98iN7yYXI/xqu3SwoQT7eL3Kk/0YF4HtfRig/B8uNQFx9ifJGIGMBptAR7R/mI6aEO/9Tny/ez1VFK/bzBGAo9ytxrVN9PdMxHaLpyEQVfexsPkktut3xYnO3ip3DkyUQ7XqB+DYt5rEQquDljYfu6pdRPauI3YP1HTA68X59dQtS8nZr3QXww0vtXTlF0M0ySEDuivQEh4NSgtaY+v3/EmI/Q6qPm3Ed2t3YEkRO0X8b99C5aXDCV5o9+ICUCu7sPmRx+3EjmxMeh7F5hlVYj0UGKvloqo8ZVRru7/YhiJtGWD424GR8SZfnFR058LB+Dxh/HY3hYPAQrdpbu7Fn7PX/ckjNUkf3VnO87Jt0HwwvO2RFbi4/4pXJGsVvVUm3jfQegyHYx5naRhfoJvjofVo9J9PMnN9sl2SSqsRxLeZzw6w9pynm3xGcDBse+68r5tPYn71DfoeMi1vnwtB+v4cHNRMuOe56aCoIZVo5oOkpHgduRQ7JBoaNHomM+JI1aKfwzZxZlyb6Mvaw4WT5Yev+G3JH8ee2y2ZTuyNsX8Mf6YmWKvtyxD390NfjK1dsdW4uPeAWcRiMa1EytVlVKVQukU9mYsZUJY2LjM+IOiN5lYO/DM9nZLmFEWb7e6zA2+ffqP4zKJp0UfEPskRTidvG2Wz78aaB+PLJEY0/4qeJ4NhrzEStu9ndacc7K1ls+mo420KofPiGPW0XYtOkEzDqzKJtaw6vXWkxDSQV/zNquUugtzeh2yBfvkdt/iuJ9V5fSQJYTwNU9ddwubhWTr0UTTp2zRPG6queltLjyn4purUY3JU4CWm6XvMJOitfmYj7Eu13jX+Nj6zWBMomUC76NMhZBFmpimEPSdru0v/ZnYvjxth/TitoiCRIfvpNISknLx/p/nEMjv7iIlnziq8w67+0/0f7f96Xq528jZ/NB3e9m+y0fOXkULzqP8InOvkd8E3e6wkReX4/PrdJrpNIFl9fN57Lu1FqblLGlGvYOOI1TgGJU7hKVqH2r7nZDm7VV/nwmf2R3QqazXcT6CSHZLvNH/54K+46mstD22AkKVJx0zi00T/ZSj9EnUsb1jkwBy4ck3JG3yk7Kbm+0FgnZmRPd9sTjJiTTxtPu/vNnYgS2Ffxy4D2n0OwuVhYUnUqT6z/TrVoaM7KH17zYPO9DGjXtMsrNC686apYxzT5XwKTFd9K+SacFmuoV73hB/4ueNsppt3xkxSnmg9Fz6ASiT9vrYDQeseRvTgZbV8+jMslL+6gTde81QPFZ514D+WN37z6eEeMw0Ocok4HlIw7ot7FXx9lFbbq0SHxorEdZ4dTYtsR6C0xkiYGq3Yb/iMomT4ttrDHU+WDpoOUX/Ib6D59EGUcqiA+hZ08bGY9tiFp8iCJew+0SavmQVS9psd9krDjxZVp67FPkzo/U2dWCGxqPm448cypNWnwXLXn1tzGvbvOqeYrXXZ8dYyoY1Sn5/qbsnPjEfDAKi7rQEdm3/rqdGyldObRhPn/c0aEs7Ca0a8/+3C2YLbnpQJ123AcTJpnaaE/E1uLD6qJUfitANJaPCTN+wVvDV/e/yXrLh9bPHE159bBaD8HvObTWYervENeR/AnXDgGnRskadjJ/ZCmEbjNND6OtD6GILwoRH+2vi711yvcD+yy4v9wq/V+Mdm5mMBfl6KkzaNzJF5Pk0BddfU+7jWJFlj2BdN2S3dG1YGfWg3lv/pF2bamhAW/7SphHhVCALJ6WD3bN3Ofsyp8f2r2J0hVnrS9bp6nbGNUMwH2Sr3vxgV3qf2P9wX208eEptPvBYXRoX2a7Z2wtPuLmdonCzcBSslhr+E4jTgiux6JKqaFuFzU0hUMoIcsprCcaZkRzMR+KL5KdEcWHVcHHsTDu5MtoxYkvUuONCw23rGcMP+t23jLeaIXUADp1PrztbhexJgVfzn8D0C3YMbZDQeewVY+6ey7toeLA68X5UzWHUdx/tDAOV8Sg2QVj/hD2fpOcHVWqbaMrxIVpkKXvPE7lax6mLrOiLy3PaQ2Kj3haPhiHs31WpeYD6ZsN0r1hNX/M6+9rmBfKwawe/LGhNry7LeuOvP3p82iIZwP1kvfSmo//QZlM8q9oSUVOGbeLujWCEuZ2oZA6H5qln0MvvopaIRaID3HCtbnlI9VOVXZ3OnrqOdS1pK8p8dGpawl1+91GqrjpeXPbEy0fIdZEsXeL4v32/TTx7Jupqu/1tPrUN3nF1erBt+paBLuf80jg+aKOJ1DVwF8GXmeJk65eY7z2c8arEjxe86MntL+nIz6as8OFkxFydvnM/7mxNLZkR12bT3wwd0GkjsWx0pLrEx/u+l2UDjArxcI/n8uDdxm12zdwixXrYls66seq3zma6wv+bzuwNcxStXzmJTSqJVjnpPfWdzPa/ZL8K1oGWj5iKjIWY7M3M5YPSffOWuOgDxEYCv1isPy24btdm1s+RFJtVxhtWR9TELYoMDTcLqF4249jbkm8+nEaUXEqf11xaXjHWvHsL+oWLErmzi6irE7B19m5QfEhGTjGh55wKdVRZ1rWIZhqOeyYM8kojsZg51N3B58rwiyRLDRqbHQqAyQZTrcvDbeF4p8x5M73TcyOhvRIRV397mM0qeErHry7cfkPtHn2M/z9tTmjqEv33qrfaS3w1fqQDgXFR83Cr2nvn6bQpMNfcpG3aPKfuWuzr3cn1Sz6mjIVe4sPq7GgsZxoNbEu5sN8LQ4ty0foJKB3d6q17ggj0XhuP1It5kPEkwA3kKLqrobbJRSPiUuaKG4LOgbTw10th0gWjn9FcS0DRcQ6d+tJxfdtpNG//oxWZY+mZbmTeSE3kZUnvaz5/fL97wfHGCHGRAuvZP57Lc7wDBOX2+fWatPY31biKOzJH7ObUr/tvLutlQZtezvw+ugnv6Mh297izxvHXK75vazePhfe4H1f0a7Na6n61Qdo4Ifnc6HBBOvK456liWdcS6s6HceXq5//OmUqthYflndBbb9gxVZe3Xq3g5blQxb+/lDLh7hvtkvBu8AwYSUWc7Ik5iN1J1yye5ExlWyTeKKIJwq5k9e6s/e7XQxuQVi/g7c/Z+T/+CZF0b8ck5YP/7nA/o2461sae9eXYcJ81I9nBLbHaJA78PLsltVWiUK0NGUHY2D8ZHmaTGc3RUt2Z5+1IK9FGUSciiz/+k3qLjREYqnMLE2YxTaNPeUKze+NOukS2uboTcVUT71eKqeK9X+hLMlDiwqOp+yb59HYE329grLGnMcfB9R9zbNfMhFbiw+r3S6O9qj+WMSHMg7DooBTAyImdMyi+Dh62t+1xyTsQ0sC0hQiKLUm3EQj/m6aGUtJQsvtYSniNkKOOzHlO1rxEXr2Z9+/l9ru2Utl5afwdNfA+4qYD+1JOOeku0y5m8RzrEnqQB28DeHLRFntNJoy716VYnDZnsaEiY/8rj6XRJF7P6UqrP5KS3MjZS3+P/66qtfltLDQlwXG2Nj/QkVfq1CYBSz3mo9po3NgoF7OvOH30ITb3qWiYl8wKmP4sWfx1GMmcDYs+y9lIvYuMmax5SOnY3HMAafixSqhN7uhlg+hlLasKKutHFRba7CoklZhoKjdLil2t594zNdhySzLh+jSCxEfGsJ0Q6+zyNe4PDKhgo6de/6JQ2xrL1r01CwfVaXX0Yhz76KyztHFZzA29voJdd+jMslEKz6iEIeyShn8XG+7+DBYIj8Wirr7yo8Xywf53f72DSuorqaKJpxxfdILcrHxLHjyairf/x5/zZwnLLC038k3kcfTRs3/mkNHpHwacdbtEdfVvfcAyr/lG5r36XPUfdTxVC50vhX73aztMIrGNi+ggxsXEk04njINiA8LYCl7+7N70bDJJ1vQEC4eE476ekS/dvhEH/xML+JavEjndtASHyb2R4pNsskkldvk+cubxxWdImMFLXsUr3de/gPVbVpOE398jubqFox7mCYvvYcWTniUJkWyCGr0sWGN1kIZ+pNbqCgq4RH8hSuufYI2PhReHE+KssOtWBDO8GhUbppyZZ/bxZ0Ay0dxj1I+oTM3xL66XeR+60qa5NlM1Q17qeKS+zW/x65PSz5/iVp3riDK7Ui5PYbR2JN+ZlljTrb++c/cQBXtwsPPsoJjafwAn6ts5xVzKSe3gIq6GJO++R07UfkFd+gu01Q0mKh5Acl16ygTsbX40AyqNMm+k/9BI489I/A6JstHXLJdNC6ywt+v53bREwSlg8fQwsJpPCq/woqYD1g+VEmFOh8izSc+QDT7UqouuYR8LcHibPkIETstznw2I3I2O/rRgIEjqfdAX2dULSaffRM1T7+SJoUEf6rR/9jz6eCaf9K2DmUk9iaVXMpJmMVtFHWJVPU0MmyiVBVDEZr5rfzuA2pY+w1NufJxXuHXj8MTXlgtlEVT/kID5v+eutBh/yjClmFxDIGmeXGGZSjtk4r4NvftWEdlHl8tjJKN/yYidfGxe2sN7X/tepogpKjSBqJFy96kETe+Qh3yO/rG39ZK29ctoX5lk0xbURZ++DRV7H0rIGA79R9Dh3fWUNnU8wPLRDr2osHRfRi7s6W8w+lb8VUPW4sPqwhV2DHFfIjZLpbFPJjPdjlYNIJov09xD514Im2YPYjq8/qGu2ccDpp0238sGmdI3ZAUi3NINFw0yqkpxEYdeyYdHbOVKoQskUS6XZqGnEW0conp+BMx60Sxf0Po0WcQee/bws3rImKH3I3nfkq9B4+Juv6FZCAAPlLMx6gvL+OP898rpSnnB6urOkLKzqvh0KtZEsLgtvWUCA45i6mr5xAdXPFF4L0enloeZ8FcESJHDh8kadYZNIrqeBG3FZ2ncdE19vAcmnhkDq164gwa+uvZ3JW28Nn/oYq6t6mq78+p8urHDI+nof4ADVjyR/6cf/fs9grU49TreFhJxz4jiFYQdWtW1gTJFOx9dbfKsB0yMcRm+RCeWxRwasTtEio+Rlz+V6rqczVtvuBLfnEd9NuFNPH296LcfHQTp5xa820SSG0rEDMdJ0zUh9ytiudGtMGvkQKx/RkrIk6hrXxuQWdLG6A5VYSG0YBT7x5fZU0/vY+sivwlyancAzrWNUd7f5d4cyTb57Yo3hmsb9FBaqX1i74KW7bmv/+hEqrjKar7L/uGptzyOk26/R1aN/0VOirn0sjWZbT0k+fpaMMhLjwYldueocOHjAe0rnzzfm6JYRl/Ey4OrxUTT3oO8tncelIdNR6pp0zD5uLDGkJLk4sT+QEqNLcyhanZqjofkQkVTB2LulDltX+lAe3BULH4T6NOtbV5totICmqP+KPooBxyly4cr2YLngkbMP0NZ1Yw88WVba0rwkHRiw8xQLz+QB31oMgTrMPlojbFvkv+QdbSwefCGupWxjk0rJodtqx3nc86srHkdOozeFTg/ZHHnE7LB17Lnxcvf5ZWz35J8b01nxurtMsqlo7f6auzsf/Y34VZXuJNp64lgflj54bllGnYW3xYlWobWqBLmKhrBmgXnIkoZBI44xgVCPEekcLVkmJxDolGcXQmIsAzxRCLjDmcjjhYPswjWj5cWdF169WiyemLT4hGfIh1T+r3GeuNwprkHTj9WT7BLRj/iKEeUPHGW+ArNObHXyW2694faGvNUqp+9Q9cXLHsk0H1VfyzjqOD8XZ+Rpx5C68SOtC7hSYv/x1/j7W5Z3SveU03iH7Z12/Q1j+MopIXJvLy9KuzR9PYEy+kZLA7ux9/rN+2kjKN5B9tmVBkzKKo6nj1dtFckW62S3KCJUVXS/Lvw1KosVyC9gYrduUP4kw2iiDTUIEhiI9o0kr5KqP5jhAn4RKEiBXXn/wLnw1fRifgVDGBCudYW0sw/T1SzEfZlJOp8++20uQZ/5MS5jVnkVJ8ZE31xbEMca+nfq8fRxXr/0ybXriS175ggbLseB06eVrYeljWyYpuQVHCypYfufA/1Cxn0QDvFs2y5Wyfln57R6CrMBNmHWb82bLMGbMc6TiIP7btWUuZhq3Fh1UxHzE1ktN1uzjiG/Mh9G+x9G8I3TqyXWInQfviwM8+pvmdz6Ccy4Olo5OFouZNSHCkaPnwRN1TyPz5L54nWVnWpp+WDh5tKtulVaixI6bJtrUoO/1q4a9ZEtzPwWNsXrdgJkciyWmvcspgwqJsyim0xdFXscz4o99R0+yH+fP1HSdrFvXqNf2WwPPVueOo//BJPCiVUV+tXt6+dvv6QPbP5p9+QXm/WUMDRpZTspC7+roz5x5MTMBvIrGV+GBFa+b//VLauWmVxRVOrZsYHIKp2bI8dWF8K09+Jfi+RxQfjpQQHwrBZXvxkfgiY/2GT6Qpv3qNt4ZPNuLxH9q0ULLA8uGIkMYa6fjMitHywZJrIy6j43ZpbhREhrAPXJ/66kewMt7i+R62biE117eO4DHWYeTpio9Mx65FSUF7lVPGtpzBPOD3UOXdtEPqSVU9L6eFHU/in41tmscf3YOC1UVDKR0ylgvpw5RPuac9yN9zjfgJf+x+cJnqd/Zu9GVQbXb056LDyoDiaCjo40vh7drkSzvOJGwlPrJeOZumHPiQ6F/nWOp2sdQkJ1zcnFZ0iQ25qHTpNSTw3OsJpuMZTheM9ySIgNO0aCyXCBSptmHZLq6ITebiQW7HYIt7V0jND7Ms6uzruLsiZ7zmMnpul6YjvhocHFnmMREbHxxPgz2+uhCsWRlLiV7WQf3OPdSapKj7E3I92D/jVUoEnXoErRwNRb4CXuNOvpj63L+WKn/+D+ox4wHF8gMrtYvKMZiQ7vi7HTSkPTXW3+q+r2crz4IJpXGH78b0QL6v/HmyKRk0jj/28u7m6caZhK3ERwnt44+9ZX91RIvER5wmhtALrtWT2Ihjz6KleZVU3b89dz1ORF3h1IYTrja2OlV9iG6XELeg0vIRnfioP/HRQP0GM66Rql5XUPXgW2O+6Rh6xT9pwZg/UL8bg3VyqntcpFhGktUby21Y9h2V/F+wIqrkbqK2t66mQZ5N4QtrCJgwa5JQFdUp1DNhVoch46dSIujcNRjzkT3Y19k1dP8vKPKJtpU546hria8kux7i79S1Vz9ekdopybRl+fdhy2btW8MfW4uTb/ljFJeU0mHK4+PdtXEFZRL2LjJmWbKLhRODcKFwmigCZBRxPme+0nG/+czybVgW82Fzy4eIHXWYGF8R2lNFKT6iO/9YZ9mmCTuosr0KplEqrxcaLcYAK8k++dxfKd4rnf4ropdfj+wa+uCXipesjgULplQ7ZZo69udluiMWGRM66DqFTJ42ydqsHj2Ym2Vx+d+otW4TlZ98ieoyw695mqo/+if1rfxpVNvYmT+Cehz9LzVsrCYSKlMzOh31ibfcXsHU3WQiORy0y9WPCt1r6MDWlYr4kyVfvEIDfriT1g69gSouvo/SDRveTsVDfTgs24ai8FdcxEeMP3ki3S52nHFV+mrYdV+Ix2posS+FJSSGY9pffjtVcIYEsUrkpVXff0zrFs9RvO93rYiwtFA1RlzyR5rX9VxaOOlx5bZCYj5yR5/FH1lbeIfQwyYRTeVEJpx2FVVc/qCmZamgsDOfbHu191UxS2vJBP6Ys2ex4n2P20193Nv4824Dfe6OVKC+yGeFyV72Ck8xZqz64RMa/8NN1ImO0MiamYFlF374DC38+DlKB2xt+bAq5kMMEg3fiDnXiZg+57Ssk6OVE7r578PyER3d6KC9xYee20XYH5EqlaYTobFXee566j/7Yv68ceg2yisoMr3Owk7FVP6LWbRv11aihXdoul3GHHce1XQsppKBo8m1J1jSu82ROMtHIigcXEm08QnqfVRZFbZ2Ww31ltp4v56e/aMTNvGg12m/puZ/fUyjWxZT1Qu3kKvHcBq2xBdAy+goNfGqrW0tTTRp0W/4e7VjTqCS0sGUytjc8mEROndeZv3CcjzcLoraIYm/UJvaB7B8qGLHPjdKy4fLFseJS4i1YBR6g0GRB2p9tSeiJTS7JfQ1O0+HTTqR18hwCsG0ngwTH/1HH0Nu2UHd6QDt2RG0ILGuyIwdrj5hVqFkUjp4NK0Y7wu0rdz1Mk1ecjcVUiOtzhpF9eTrVVS7aQXtXrco8J29G9WzeVIJ+13RRCxKtQ0tr67ApElYdLuY7b6ouU5Fymaq/+SwfKiSQRNsVE0W41iHJpVwhlg+gh1niXavnMsf1bI01FieGwxIZXTs1FXx2uHUdqc4s3IzVnww69FWV3/+fOfK/wbeb9rls4QczEuNTJfQjsxVpdeRV5boEBVQ1cBf0sBbP6cd2b4iZPXbV9OR7cGA1MbdNZTqpI68SwrxKa9undvFqp/HQstHAmM+kmGlSVXsuC+UdT5Cz6NUF9HRkaWT8j556T20YPNcmlz/uaF1db1wpnLd2TlUPeQ2qlj/F9WS9VruH48ztnomqci+TqNp0P5N1LplPhFdyd9z7fdN2G1dguUIUonKa/5E+2p/yUVkZXufmSMdBxLtX07uvTXkbPRlczKk/alflMzm4iMBbgWzlg+vJ77ZLkmyJjDFbqwzZmaa02PGhvvCoRdwqmzHSplCpHo7RoUHQy0g09WpV+B5lmDd0BuH15lZlg+G1Gcy0f73qaL2VVry2BZqGziNujf40mxze42gVKVribLaq1w8mFgPwZxDmyi/ZW/g/dwjsbnoEkFm3j4YxLIiYzoTg1lzcX7n7sJ3Lfp5xOHpBcfGEaN7Wm49mpGTSlxde5mKcO6EBkeK6kNOE2FWVerrtMrM51rE44ZDRNyPLo2y5HwcYtO8DHR5DZt6Ae2mbvz5+MbvacrKB6i/15fp0nWQLxsmHcjrOZw/dmnaTH3atgTe79S6m1Ide1s+LIr5UDTACiG7U4mpdbEI5YUTHiVXXical5JxFPG90DsPbbH13b4mNtwXilYDofFPaVgJt+Kqx2nnlqupor9vwlAj7g3MhP2mJz6yhc+iLV+fyhQV96DsO5bQqmX/pcPrv6MBm9+iEqrjBdX6DPSVNE8HuvYfRTSXqD9rhCecBiWePTwt16q4wXhga/FhXXn18Ivfosl/prZtC2jKtEtNr2/SWTeSlSgDTpNl+WDbNbC/03BSSQg2FB+iqA+zfCiXpHSACYveBia2RVP+RvLaj2jS4S+j3tbCCX+kSWpjEI4jrYZsYe6fDBQf/hovI485neiY06m15Xe0ZO6/qcfQyUnrYBsNPUoH8+Jy/hovWx19qI9nJ+VIbVS3Zzt16+ULrE1FbC0+rCuvHn5yTjyDmVh9ZtZUIp7da/WIrhaDVY3/MgA7ig/R7RLa1Vb0GGfYvpl4+lW0tntfoo+iFx+5xcHusCKysN+yRNdKCGLfGrFjbqaSnZNL408xf6OYbJwuF2119qaBXp/FeF/+UMpuaKGeVEf7t69LafGRPhIvld0uSYqjiIbkWT6MLidp/j4bnQP44zrXULIbyQoUTplsF50OrJlUZMyYpcfA94UKpSL9xp3Au7zWuIbpmuQVd/8ZavnIFA4KTfBauwylA9m+oOIje8Kr4KYSthYfZi9Z87qep76eFA8GFN1LqZJqu8nR38D6leKj8Jr3eBOwLlf/m+xGOpmCrcIh/M2hgZiZnnocq6/eodFxt7hHH8q6o4aG3lNteF12sHykM62dg6nBLFPnaJ7P6tW2X4ifS0Hsd0VTYM7y4eg1Nv0nhqQJJeVkcWDcjbyBlBmYCbHy6sd4Z0r7kdmTrdny6plOaCM9szg1LB/+WAdzVYftte/TjaweQfHRY2g5eYp86bjOel/2TqqSRrNm8tFK6Ut1y4eCpAacCsNwZfEGUvNG3Mtfz+90usqXEPNhlzv9SDhtUl7dT6zlvcPK0ccCzsOUpv/EU2mX1J2W5B3Da7tkFfvc0/mNqV3rAwGnppDSvgZDrJOYM7/Yoj3t22flF9xBOzf9hCb188dxCL58XPQyeoI112pAz+2SefsmN998AzkjbpdokArNlQsAiaVrSSnR/evJXz6uoMRXcr24tZZSGVuLD8nk5KY5cdso4HT86dfSgk3fkNz3WJoS0ziCz3sP1Kp7APFha8uHTp8jRQBuBu6bkr6xlfgWG8NFy4oTZlHjio9o3Nm3xLwukDi69vHdyHWT91Fba4tuSnUysbX4iMbyUd3jIhq05zNFu/N0crvEOomx/P/Jt75t+nthGQla40ijfZlYMm+CNdPhWQw+tUO2C2OX1IN6yXv486qel1OXunk0zF2TMPEx+rhzidg/kFYUl5QGan/U7thgqLZMMrD1ld50kTFJooobn6auv9sU8naqX/wszHaJegRRbBdulzQ6xqxH/Pl1//4M3TduKXhvWPnzf9DBLuOSE/MB0grJ4aA9Tp+r7MD21O1ua2vxYRb/BTA0UjxZhbvSeRLTHod2qq2dsVu2ByO/sEvgeVZ2SBO0FDmO48mugRfwx3nFZ5v+m106BcRA5rMv3+e2O1LzDaUqtpbHZmM+xIlxXrfzqbzu3yk1oRshVVxEYqVFBeK+hOUjQBodYpZR1KUbLZv6HDlcOTQ61G+d4QGnjAk/vYvWLv8RTRp/vOnvwvJhb+Sy04kWfE29a7+iVCU1ZqKkYXJyEywe/c68S3g/fe5KU93tkqn++5hJEdGYaMaeeAGNnjrDlhVfWcnvssnThLRbE5YPC7NdQPox9NhzqVV2UV/vTtq4wnhBuURizytaAJPZLoLIUHSE9HoobUgR8aElgiQhyBBulyDpZF1LODYVZolKtQXpR2GnYlpZcAx/Xvfdi5SK2PqslWJwWWTn5lM6kuqTmORtTfYQUhI73OmbIa2qCseB3VfNDz6nblRdcgnPxPMDywdwjPMdD4P3fEruttS7rtr7DDYbUyCUPGbKsqr0OqrqfRUVFfeglEaRNZCcnzx0T2uNQ/IIJwliPoKkuGhMPJkf8xGGcM707Dcs8NwtZVHFDU/SsPPvtzTVFqQ3I6aeSwepkLrSIVr13fv8vZbmRtq8egHJXtHCnBxsKz6iUYKhVoPKa/5EldeZ609i25gPg9t1eFriPpa0BK4F7ePYpsJsu+Srabmj96n8sXO3nrRg3EO0cPKfeLwIsDfZOblU0913bHT4/jHyejy05u/n0oC3ptH8//w12cOzr/hobjpqus5HJqQ7pozbRWMcOZXXJnwo6UCq/GwgiSjioYg6/PwLWjjpcZp42SOB9yaf/QuadMZ1SRgcSEUGn30vHZVzaah7Ha354wk0rrGKv5+z6YtkD82+4qOtpTmmgNN0JXmptsYqnI469szEDCfNSBnRmCrYoMJpKJK7Kaynx6SfXA8rB9CEdQBfMeh6/nxk67LA+0UtuynZ2FZ8tLY0xhRwmq4kL9U2dBzRfMu+SGhrTnZ3u+RPOF/hbgHACOMvuDtwzLAYEEY3z96kx32k/2xqglrqGnje1txkOqAxEyLsU73OB1AHRaNCsV/AKbMKbjr/C+p86w/JHgpII3Jy8yjn2k9o3oh7yXP9d/y9AqmJDh+sS+q4bHVF25vbn0qa9/HnXq/bRjEfcgoKqFQZR3oQ2tXV7tjR8sEYOKo82UMAaUj33gOo+wV38Of7qBPPgKnbsSGpmZo2mwGCk7Ds8cRU4TSjG+jFAcNdbYEqqSMaUxEcSwAY5YCrO388XKtskJpoTF3RnnrqKRozZgwVFhbyf5WVlfTpp58GPm9ubqabbrqJiouLqaCggM477zzas8fXEjoVEKtnRmP5cAh1PoBZ0NU2Fpw49pRkQPwVAMngSG5P/ti6bzMlE1NncJ8+fejRRx+lRYsW0cKFC+nEE0+kGTNm0KpVq/jnt956K3344Yf09ttv09y5c2nXrl107rnnUqogig2W82z6+7j7tAxU7DSHBLeLjtsF5yUARmkt6ON7Ur+Dkomp26kzz1SmQT700EPcGlJdXc2FyQsvvECvvfYaFyWMWbNm0fDhw/nnFRUVlEp4vR7zMR+4yEVN2J6G28UUTgScKsDhA0B0yDkd+aPD3UjJJOrZ1OPx0BtvvEFHjx7l7hdmDWlra6Np06YFlikrK6O+fftSVZWvsIkaLS0tdPjwYcW/RLhdZNYMTsOsv0vqkWEBp6kH6laYQ4L4AABYmbafZLe2afGxYsUKHs+Rk5NDN9xwA7377rs0YsQIqq2tpezsbOrUqZNi+R49evDPtHjkkUeoqKgo8K+0tJTih+h20Y758JKDqnpdHvY+xEeCQcxHACfcLkoEK6TR0v0AACJZUq+Ym/LiY9iwYbR06VKaN28e3XjjjXTFFVfQ6tWrox7A3XffTfX19YF/27dvp3ghXqK45cPw0mke85GKkzgmDFM40vXYS4jlDPsGAMOkSMFC07ZcZt0YPHgwfz5x4kRasGABPfHEE3ThhRdSa2srHTp0SGH9YNkuJSUlmutjFhT2L+HZLpECTlUmRwcsHxaCCcMMiDfSCViGjgXAMJLKfJgMYr6ieb1eHrfBhEhWVhZ99dVXgc9qampo27ZtPCYk5ep86KTaalXjhNvFOmD4MEdWdmIEenoeQDiYADBMwIrqTR/LB3ORnHbaaTyItKGhgWe2zJkzhz7//HMer3HNNdfQbbfdRl26dOF1QG6++WYuPFIl00W8RLlbWKSvnjtCxfIBv3tCSUFnUdJwZWUnewipC5QsACbwnS9Skt3xpsTH3r176fLLL6fdu3dzscEKjjHhcfLJJ/PP//rXv3LfNCsuxqwh06dPpyeffJJSBdHMNO6/P9dv0KRyQYPp20IwYRjmMOW1t4MCAXAuAhDjuZNG4oPV8dAjNzeXZs6cyf+lJsqdXSrvMvXttA04TUUgPoBlhw+OJQBMnzzplmqbzhgtKsZS99QqcKZvefX0cmA0yr74hu7jTkv2UFIETK6h2LWxHABWWT6kdIr5SHdibbCGgFPrkHR0r/vWNbS1disNKpuQ0DGlKuklHRMPSvUDYIbUsHzYSnyY2dlqhYscTlsZiuKLzt1qYadi/g8ATVBkDICo8IcPJNvyYavZ1LDbBam2cQfl1YF11g4cSwCkm+UD4sMEKDIGkoGWGLYzkgMxHwDEFvMB8ZFAjO5s9YsZ6nxYCCYMw0B8AAAsd1mme4XTdMJMURW1ZVHnwzrgdgGxIZ6LOJYASLdrr61mU7hdUmffI0PBDNhXoSDVFoAY3S6wfKRiwKlWtkt6Jgcl27fH6EwNyR4CyCQgOACISbgne16wlfgwUzFB1e2CCqfWgcnDMMmXjqneVw7HEgBGkRHzkXiMKz31i5kTAafWgQkDJKBgHQBACSwfaVjnAzEfIBkg24V0g79l7B4ADOPK68SbqjZ36EHJJD2DGOKc7eKZ8RTRgv+Efx9uF+uA5cMwEB8R/S5JHAgA6cX4Uy4lOuVSKk3yOGw1mxqxfFT1u4EGjz1W9TNnmgacJruSnRrIdjGO116naVqlCwIAosNmVzUTvV1UloXbxTpgRTIOLB8RQP0dANIOW521hmI+dO6oHJgwQRKQ7XWamo75gDQDIP2w1VXNiPjQi5zH3bqVYMowihcuhjBQZAyA9CZNgxjiF3CqFjlf3ecakop6UXl8hmVL4LM3A/aVbswQjiUA0g57iY8olxp38R8oN68gDiOyMZgwDOOF+AhH7GqL/QNA2mErP4JEXltOlMkuJgNiAzEfmX1+AmBHbHVVMxZwGr5L4CKwHuxTEBs4fgBIZ2wlPoyAS1qiwJ42iheppBHEK44lANINW13VHEbcLv6LmhCcKqb1AWuA5cM4qPMRDrJdAEhvMKuGonIhw0QJksFBKuSPtd1+lOyhpDg4PwFIN2yW7WIk8BLiIyHAmhSRlmu+oXnV79O4M36e7KGkHLBGApDe2Ep8OJD1kTJA0EWmpHQwlZTenuxhpCQ4fgBIb2x2+xBdefX0v8uC6AKZBcQHAOlNus+qprCt2yUVu9qm+S4FAAAQPbYSH4bcLoFZUcx2wUxpOWlvTQJJBccPAGmNzc7gKC0faChnORB0ICZw/ACQ1thqVjXSWE5YOo4jAQDEAsQrAOmNrcRHtG4XEOeupACYBOIDgPTGVuIj2oDTdCcl/yJMHsAqcCwBkHbYSnwYAXdUCQIBgyAGcJ4CkN7YagYw1dsFAJAe4iMFU8kBAPrYSnwYkxUQH4kAGg/EAiwfAKQ3NhMfJu6QcDcVVzB5AMvAsQRA2gHxEbYQLmQApDrp3/IAAHtjqzPYrtkuKZk2jMkDxABiPgBIb2w1A8DykTo4HLZqqAysRjxPcc4CkHbYSnw4JbtaPlIPpwviA0QPYoYASG9sJT5A6uBwQnwAAIBdsY34kL0GanzgjiphQHyAWEDAKQDpjW3OYK9B8QH/cWJwQnyAGMBNAgDpjW3EhywbFB+I+UgIsHyAWID4ACC9sZH4kE1e1DIpfS/1/haH05nsIYA0Bm4XANIb25zBXq/H0HIy7qjiwsJJjyteOyE+gGXgnAUg3bCN+DBq+QDxYdJPrif5dwdpaV4lLep4AuUVFCV7SCCNgdsFgPTGPo53w+Ij8y5qUooIL8nhoHG/+SzZwwAZJz5S4/gGABjHNpYPo24X3FEBkPowIQsASF9scwYbd7u0iw/cTAEAAABxAeIjlIy0fEBJgUwmE89ZADIb24gPw0XG/OB6BgAAAMQF24gP03U+YCwAAAAA4oJtxIeds10AAACAVMI24kM2mO2SiTEfjYWDkj0EAAAAwH51PuwccDri0sep+lUHdZryMypL9mAAAADYHhuJD/t2te1Y1IUq/ue5ZA8DAAAAsJfbxXS2C5ldHgAAAABGsI34MJq+IiHgFAAAAIgr9hEfXvvGfACQyaAlAgDph23Eh9dozId9dgkAAACQFGwz0xoNOMVNFAAAABBfbCQ+ULIUgEwE5zYA6Yd9xIeNi4wBAAAAqYR9xIfheFOIDwDSCZyzAKQfthEfRtWHbKNdAkBGIOGcBSDdMHXWPvLIIzR58mTq2LEjde/enc4++2yqqalRLHP88cfzOxHx3w033EDJRpY9pu6iJPiRAUgPYPkAILPFx9y5c+mmm26i6upqmj17NrW1tdEpp5xCR48eVSx33XXX0e7duwP/HnvsMUo2stE6HwCA9AKWDwAyu7fLZ599pnj94osvcgvIokWLaOrUqYH38/LyqKSkhFIJ2WCFUwBAeiFBfACQdsR01tbX1/PHLl26KN5/9dVXqWvXrjRq1Ci6++67qbGxUXMdLS0tdPjwYcW/ZGa74EIGQJqBcxYA+3S1ZY3abrnlFjr22GO5yPBz8cUXU79+/ahXr160fPlyuvPOO3lcyDvvvKMZR/LAAw9QvPGHcByVc+mgozP1kXerLwj/MQBpBbJdALCR+GCxHytXrqTvvvtO8f71118feD569Gjq2bMnnXTSSbRx40YaNGhQ2HqYZeS2224LvGaWj9LSUrKc9gqnbZKLdgy9jPrUaMSh4EIGQFohw/IBgD3Exy9+8Qv66KOP6Ntvv6U+ffroLlteXs4fN2zYoCo+cnJy+L9ElVeXeddabYEB7QFAegHLBwAZLj5YGeObb76Z3n33XZozZw4NGDAg4neWLl3KH5kFJBVKMDPxgYsVABkELB8AZLb4YK6W1157jd5//31e66O2tpa/X1RURB06dOCuFfb56aefTsXFxTzm49Zbb+WZMGPGjKGkIlo+dMUHLmQApBOSw5nsIQAA4ik+nnrqqUAhMZFZs2bRlVdeSdnZ2fTll1/S3/72N177g8VunHfeeXTvvfdSsvG21/mI5HaB3wWANAPnLACZ73bRg4kNVogsJTFo+ZD0hAkAIOXI79Yv2UMAACQq2yXdEGM+YPkAIP1ZNvU5atq9lirKpyd7KAAAk9hIfPgsHwCAzGDsiRckewgAgCixT3Rlu+XDy/5kPbcLLB8AAABAXHHYz/Jh0O0CSwkAAAAQF+wjPrz+gNNI1g3b7BIAAAAgKdhmpvV3teWlmEPER1XpdYHn8LoAAAAA8cU24oMEy4fodmmSs2nwab8ILgf1AQAAAMQV+4gPlFcHAAAAUgL7uV3IQbIgPrgYEQNQIUwAAACAuGIf8dHudvERIjAcwdeSfXYJAAAAkBQctqtwKindLnlSi3JBWD4AAACAuGLDmA/2J4cKDMHyAfEBAAAAxBXbiA9Z9vge2X8hAgOCAwAAAEgctrN88L61emIDQgQAAACIKw67aQ81t4soRmAFAQAAAOKLbcQH6bpdwndDVr/JiRoZAAAAYCtcZMNsF92utu1WkQmnXkUL3W3UvayC+iZslAAAAEDmYxvxITkc1CjnkFvK0Q049RcjY8tPOuuGhI8TAAAAyHRs43YZPfUcyntgLw27d56yoiniPAAAAICEYhvxocCA2wUAAAAA8cGe4iP0z1YJOAUAAABAfLDlrKvrZYELBgAAAIgrthQfYQJDfB0oRgYAAACAeGBL8REaYIqAUwAAACBx2FJ8+KqcagAhAgAAAMQVW4oPXcsHxAcAAAAQV2wpPkKB2wUAAABIHBAfoUCIAAAAAHHFluJD9rq1u9qiyBgAAAAQV2wqPnwdblXFh8OWuwQAAABIGLacaUPFhxJYPgAAAIB4YkvxQWGWD3vuBgAAACAZ2HLW1bN8IPMFAAAAiC+2FB/hlg/U+QAAAAAShS3FR5jlQww4hfgAAAAA4oo9xYfs1fkU4gMAAACIJ7YUH7puFwAAAADEFVuKj/A6H8HdACECAAAAxBdbig+Sdep8IO0WAAAAiCv2nGnhdgEAAACShj3FR0jAqaK8OoQIAAAAEFdsKT70i4wldCgAAACA7bCl+AiN+VCUV0fMBwAAABBXbDnT5vefrPkZ3C4AAABAfLGl+Bg99RzFawgOAAAAIHHYUnxIDgfVuIapB5yiwikAAAAQV2wpPnyoi4zufYcmfCQAAACAnXCRTZEF8cEsIUdu20KetlYqyu+Y1HEBAAAAmY5txUdoTm1BYeekDQUAAACwE7Z1u8jJHgAAAABgU2wrPgAAAACQHCA+AAAAAJBQbCs+kFALAAAAJAfbig+SEfUBAAAAJAP7ig8AAAAAJAXbig8J+S4AAABAUrCt+ECyLQAAAJAcbCw+AAAAAJAMbCs+4HYBAAAAkoNtxQcAAAAAkgPEBwAAAAASim3Fx/7uFfyxUc5J9lAAAAAAW2HbrrbjL3uU5n1YSqVTZlBesgcDAAAA2Ajbio/cDvlUfsFvkj0MAAAAwHbY1u0CAAAAgOQA8QEAAACAhALxAQAAAIDUFR+PPPIITZ48mTp27Ejdu3ens88+m2pqahTLNDc300033UTFxcVUUFBA5513Hu3Zs8fqcQMAAADADuJj7ty5XFhUV1fT7Nmzqa2tjU455RQ6evRoYJlbb72VPvzwQ3r77bf58rt27aJzzz03HmMHAAAAQBoiybIcdZ3xuro6bgFhImPq1KlUX19P3bp1o9dee43OP/98vszatWtp+PDhVFVVRRUVvtoaehw+fJiKior4ugoLC6MdGgAAAAASiJn5O6aYD7YBRpcuXfjjokWLuDVk2rRpgWXKysqob9++XHyo0dLSwgcs/gMAAABA5hK1+PB6vXTLLbfQscceS6NGjeLv1dbWUnZ2NnXq1EmxbI8ePfhnWnEkTCn5/5WWlkY7JAAAAABksvhgsR8rV66kN954I6YB3H333dyC4v+3ffv2mNYHAAAAgAyscPqLX/yCPvroI/r222+pT58+gfdLSkqotbWVDh06pLB+sGwX9pkaOTk5/B8AAAAA7IEpyweLTWXC491336Wvv/6aBgwYoPh84sSJlJWVRV999VXgPZaKu23bNqqsrLRu1AAAAACwh+WDuVpYJsv777/Pa3344zhYrEaHDh344zXXXEO33XYbD0Jl0a4333wzFx5GMl0AAAAAkPmYSrWVJEn1/VmzZtGVV14ZKDJ2++230+uvv84zWaZPn05PPvmkptslFKTaAgAAAOmHmfk7pjof8YANmsWLsMBTiA8AAAAgfcQHy1hlcZ9MhFgecBpPGhoa+CNSbgEAAID0g83jkcRHylk+WP0QVpKdxZRouXnSUQnCkpM88BskH/wGyQe/QfLJ9N9AlmUuPHr16kUOhyO9LB9swGL6bqbADrRMPNjSCfwGyQe/QfLBb5B8CjP4N4hk8bCkvDoAAAAAgFkgPgAAAACQUCA+4gyr3nr//fejimsSwW+QfPAbJB/8BskHv0EKB5wCAAAAILOB5QMAAAAACQXiAwAAAAAJBeIDAAAAAAkF4gMAAAAACQXiIwK///3veaVV8V9ZWVngc9ZIj3X7LS4upoKCAjrvvPNoz549inVs27aNzjjjDMrLy6Pu3bvTHXfcQW63W7HMnDlzaMKECTwKevDgwfTiiy8m7G9MB3bu3EmXXnop38+sg/Lo0aNp4cKFgc9Z3PTvfvc76tmzJ/982rRptH79esU6Dhw4QJdccgkv7sP6B7EOzEeOHFEss3z5cvrxj39Mubm5vBLhY489lrC/MZXp379/2HnA/rFjn4HzIP54PB667777aMCAAfwYHzRoED344IP82PeD8yD+sAqet9xyC/Xr14/v42OOOYYWLFgQ+By/gUFYtgvQ5v7775dHjhwp7969O/Cvrq4u8PkNN9wgl5aWyl999ZW8cOFCuaKiQj7mmGMCn7vdbnnUqFHytGnT5CVLlsiffPKJ3LVrV/nuu+8OLLNp0yY5Ly9Pvu222+TVq1fL//jHP2Sn0yl/9tlnCf97U5EDBw7I/fr1k6+88kp53rx5fH99/vnn8oYNGwLLPProo3JRUZH83nvvycuWLZPPOussecCAAXJTU1NgmVNPPVUeO3asXF1dLf/3v/+VBw8eLF900UWBz+vr6+UePXrIl1xyibxy5Ur59ddflzt06CA/88wzst3Zu3ev4hyYPXs2m/Hkb775hn+O8yD+PPTQQ3JxcbH80UcfyZs3b5bffvttuaCgQH7iiScCy+A8iD8XXHCBPGLECHnu3Lny+vXr+RxRWFgo79ixg3+O38AYEB8RYAcWO0jUOHTokJyVlcUvAn7WrFnDL8pVVVX8NbvIOhwOuba2NrDMU089xQ/WlpYW/vo3v/kNFzgiF154oTx9+vQ4/VXpxZ133in/6Ec/0vzc6/XKJSUl8uOPP674bXJycvhJy2CTGftdFixYEFjm008/lSVJknfu3MlfP/nkk3Lnzp0Dv4t/28OGDYvTX5a+/OpXv5IHDRrE9z3Og8RwxhlnyFdffbXivXPPPZdPUAycB/GnsbGRC2ImAEUmTJgg//a3v8VvYAK4XQzATGasUc7AgQO5qYyZjxmLFi2itrY2blbzw1wyffv2paqqKv6aPTIXQY8ePQLLTJ8+nTcYWrVqVWAZcR3+ZfzrsDsffPABTZo0iX76059yc/348ePpueeeC3y+efNmqq2tVexD1l+gvLxc8Tsw8yZbjx+2POslNG/evMAyU6dOpezsbMXvUFNTQwcPHkzQX5v6tLa20iuvvEJXX301d73gPEgMzLz/1Vdf0bp16/jrZcuW0XfffUennXYaf43zIP4wNyFzfzFXiAhzr7DfAr+BcSA+IsAOGuZ3/uyzz+ipp57iBxfzwzG/HzvI2MHBDiQRdoFlnzHYo3jB9X/u/0xvGXZhbmpqIruzadMmvu+HDBlCn3/+Od144430y1/+kl566SXFflTbh+I+ZsJFxOVyUZcuXUz9VoDovffeo0OHDtGVV17JX+M8SAx33XUX/exnP+PCLisri4twFnvAbogYOA/iD+u2XllZyWNtWPd1JkSYEGdiYffu3fgNTJByXW1TDf9dBWPMmDFcjLBAo7feeourXRB/vF4vv0t4+OGH+Wt20V25ciU9/fTTdMUVVyR7eLbjhRde4OcFswaCxMGuOa+++iq99tprNHLkSFq6dCkXH+x3wHmQOP71r39xq1/v3r3J6XTyAOmLLrqIWwCBcWD5MAm7uxs6dCht2LCBSkpKuAma3QWKsCh/9hmDPYZG/ftfR1qGRUJD4BCPGh8xYoTiveHDhwfcX/79qLYPxX28d+/eMBMqizo381vZna1bt9KXX35J1157beA9nAeJgWUH+a0fzIV12WWX0a233kqPPPII/xznQWJgWUZz587l2Snbt2+n+fPnc7cjc8vjNzAOxIdJ2AG3ceNGPiFOnDiRmz+ZH9YP88mxSZGZ5hjsccWKFYqDbfbs2fyC6p9Q2TLiOvzL+Ndhd4499li+X0WY35tZoBgs9ZCdkOI+ZKZ65j8Vfwc2OYp3J19//TW3qjBrln+Zb7/9ll9IxN9h2LBh1Llz57j/nenArFmzuMmYpcz6wXmQGBobG3lcgAi782bHMAPnQWLJz8/n8wCLwWDu4BkzZuA3MIOZ6FQ7cvvtt8tz5szhqW3ff/89TxVkKYIs9dCfYti3b1/566+/5imGlZWV/F9oiuEpp5wiL126lKcNduvWTTXF8I477uBZAjNnzkSKocD8+fNll8vFUw1Zaturr77K99crr7wSWIalt3Xq1El+//335eXLl8szZsxQTW8bP348T9f97rvv5CFDhijS21hUOktvu+yyy3h62xtvvMG3k0npbbHg8Xj4sc6i7kPBeRB/rrjiCrl3796BVNt33nmHX4tYlpAfnAfxhx2PLDuFHa9ffPEFz4YsLy+XW1tb+ef4DYwB8REBlurXs2dPOTs7m5/47LVYX4IdUP/zP//D06LYwXHOOefwOggiW7ZskU877TSep80uFkzQtLW1KZZh9RLGjRvHtzNw4EB51qxZCfsb04EPP/yQT14sZa2srEx+9tlnFZ+zFLf77ruPn7BsmZNOOkmuqalRLLN//35+grPaCCzF86qrrpIbGhoUy7C8fJbWy9bBfm92IQE+WG0Vdr8Sul8ZOA/iz+HDh3mKMxN5ubm5fP+w9E4xHRPnQfx58803+b5nxyhLq73pppu4WPCD38AYEvvPlKkEAAAAACAGEPMBAAAAgIQC8QEAAACAhALxAQAAAICEAvEBAAAAgIQC8QEAAACAhALxAQAAAICEAvEBAAAAgIQC8QEAAACAhALxAQAAAICEAvEBAAAAgIQC8QEAAACAhALxAQAAAABKJP8PKkKmgR/+Ry8AAAAASUVORK5CYII=", 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" ] @@ -367,9 +459,11 @@ "wave = pipe.telescope.wave_seq\n", "\n", "spectra = rubixdata.stars.datacube # Spectra of all stars\n", + "spectra_sharded = shard_rubixdata # Spectra of all stars\n", "print(spectra.shape)\n", "\n", - "plt.plot(wave, spectra[12,12,:])\n" + "plt.plot(wave, spectra[12,12,:])\n", + "plt.plot(wave, spectra_sharded[12,12,:])" ] }, { @@ -381,16 +475,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, diff --git a/rubix/core/data.py b/rubix/core/data.py index 6804b19d..213a13a5 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -64,7 +64,7 @@ # Registering the dataclass with JAX for automatic tree traversal -@jaxtyped(typechecker=typechecker) +#@jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class Galaxy: @@ -81,18 +81,18 @@ class Galaxy: center: Optional[jnp.ndarray] = None halfmassrad_stars: Optional[jnp.ndarray] = None - def __repr__(self): - representationString = ["Galaxy:"] - for k, v in self.__dict__.items(): - if not k.endswith("_unit"): - if v is not None: - attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - attrString += f", unit = {getattr(self, k + '_unit')}" - representationString.append(attrString) - else: - representationString.append(f"{k}: None") - return "\n\t".join(representationString) + #def __repr__(self): + # representationString = ["Galaxy:"] + # for k, v in self.__dict__.items(): + # if not k.endswith("_unit"): + # if v is not None: + # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + # attrString += f", unit = {getattr(self, k + '_unit')}" + # representationString.append(attrString) + # else: + # representationString.append(f"{k}: None") + # return "\n\t".join(representationString) def tree_flatten(self): """ @@ -122,7 +122,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -@jaxtyped(typechecker=typechecker) +#@jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class StarsData: @@ -154,18 +154,18 @@ class StarsData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - def __repr__(self): - representationString = ["StarsData:"] - for k, v in self.__dict__.items(): - if not k.endswith("_unit"): - if v is not None: - attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - attrString += f", unit = {getattr(self, k + '_unit')}" - representationString.append(attrString) - else: - representationString.append(f"{k}: None") - return "\n\t".join(representationString) + #def __repr__(self): + # representationString = ["StarsData:"] + # for k, v in self.__dict__.items(): + # if not k.endswith("_unit"): + # if v is not None: + # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + # attrString += f", unit = {getattr(self, k + '_unit')}" + # representationString.append(attrString) + # else: + # representationString.append(f"{k}: None") + # return "\n\t".join(representationString) def tree_flatten(self): """ @@ -206,7 +206,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -@jaxtyped(typechecker=typechecker) +#@jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class GasData: @@ -244,18 +244,18 @@ class GasData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - def __repr__(self): - representationString = ["GasData:"] - for k, v in self.__dict__.items(): - if not k.endswith("_unit"): - if v is not None: - attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - attrString += f", unit = {getattr(self, k + '_unit')}" - representationString.append(attrString) - else: - representationString.append(f"{k}: None") - return "\n\t".join(representationString) + #def __repr__(self): + # representationString = ["GasData:"] + # for k, v in self.__dict__.items(): + # if not k.endswith("_unit"): + # if v is not None: + # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + # attrString += f", unit = {getattr(self, k + '_unit')}" + # representationString.append(attrString) + # else: + # representationString.append(f"{k}: None") + # return "\n\t".join(representationString) def tree_flatten(self): """ @@ -300,7 +300,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -@jaxtyped(typechecker=typechecker) +#@jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class RubixData: @@ -317,11 +317,11 @@ class RubixData: stars: Optional[StarsData] = None gas: Optional[GasData] = None - def __repr__(self): - representationString = ["RubixData:"] - for k, v in self.__dict__.items(): - representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) - return "\n\t".join(representationString) + #def __repr__(self): + # representationString = ["RubixData:"] + # for k, v in self.__dict__.items(): + # representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) + # return "\n\t".join(representationString) # def __post_init__(self): # if self.stars is not None: diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 310aae30..f4fa06b2 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -4,14 +4,20 @@ import jax import jax.numpy as jnp +from jax.tree_util import tree_flatten, tree_unflatten +import dataclasses # For shard_map and device mesh. import numpy as np from beartype import beartype as typechecker from jax import block_until_ready from jax.experimental import shard_map +from jax.sharding import NamedSharding from jax.sharding import Mesh, PartitionSpec as P from jaxtyping import jaxtyped +from functools import partial +from jax import lax +from jax.experimental.pjit import pjit from rubix.logger import get_logger from rubix.pipeline import linear_pipeline as pipeline @@ -31,6 +37,7 @@ from .rotation import get_galaxy_rotation from .ssp import get_ssp from .telescope import get_filter_particles, get_spaxel_assignment, get_telescope +from .data import RubixData, Galaxy, StarsData, GasData class RubixPipeline: @@ -171,7 +178,7 @@ def run(self, inputdata): return output - def run_sharded(self, inputdata, shard_size=100000): + def run_sharded(self, inputdata): """ Runs the pipeline on sharded input data in parallel using jax.shard_map. It splits the particle arrays (e.g. under stars and gas) into shards, runs @@ -189,7 +196,7 @@ def run_sharded(self, inputdata, shard_size=100000): jax.numpy.ndarray The final datacube combined from all shards. """ - time_start = time.time() + #time_start = time.time() # Assemble and compile the pipeline as before. functions = self._get_pipeline_functions() self._pipeline = pipeline.LinearTransformerPipeline( @@ -200,94 +207,105 @@ def run_sharded(self, inputdata, shard_size=100000): self.logger.info("Compiling the expressions...") self.func = self._pipeline.compile_expression() - # --- Helper: Shard the particle data --- - def shard_subdata(subdata): - # subdata is expected to be a SimpleNamespace with attributes that are arrays. - new_subdata = {} - for attr, value in vars(subdata).items(): - if hasattr(value, "shape"): - n_particles = value.shape[0] - n_shards = n_particles // shard_size - # Truncate if needed. - new_value = value[: n_shards * shard_size] - # Reshape so that the first dimension indexes shards. - new_subdata[attr] = new_value.reshape( - (n_shards, shard_size) + value.shape[1:] - ) - else: - new_subdata[attr] = value - return SimpleNamespace(**new_subdata) - - # Create a new sharded input object. - sharded_input = {} - # Assume that 'stars' and 'gas' contain particle data. - for key in ["stars", "gas"]: - if hasattr(inputdata, key): - sharded_input[key] = shard_subdata(getattr(inputdata, key)) - # Preserve other parts (e.g. galaxy and units) as-is. - #sharded_input["galaxy"] = inputdata.galaxy - #for key in vars(inputdata): - # if key not in sharded_input: - # sharded_input[key] = getattr(inputdata, key) - sharded_input = SimpleNamespace(**sharded_input) - # ----------------------------------------- - - # Determine the number of shards from one batched array (e.g. stars.coords). - n_shards = sharded_input.stars.coords.shape[0] - devices = np.array(jax.devices()) - if n_shards != devices.shape[0]: - raise ValueError( - f"Number of shards ({n_shards}) must equal number of devices ({devices.shape[0]})." - ) - mesh = Mesh(devices, ("x",)) - - # Define a function that will process one shard. - def pipeline_shard_fn(shard_input): - # Here, shard_input is a dict (or nested namespace) for one shard. - output = self.func(shard_input) - # Assume output has a 'datacube' attribute. - return output.datacube - - # Convert the sharded input namespace to a dict. - def to_dict(ns): - if isinstance(ns, SimpleNamespace): - return {k: to_dict(v) for k, v in vars(ns).items()} - else: - return ns - - sharded_input_dict = to_dict(sharded_input) - - # Create partitioning specifications for all array leaves. - def create_sharding_spec(val): - if hasattr(val, "shape") and isinstance(val, jnp.ndarray): - return P("x") - elif isinstance(val, dict): - return {k: create_sharding_spec(v) for k, v in val.items()} - else: - return None - - in_shardings = jax.tree_util.tree_map(create_sharding_spec, sharded_input_dict) - # Assume output datacube is sharded along 'x'. - out_shardings = P("x") - - # Use jax.shard_map to parallelize across shards. - sharded_pipeline_fn = shard_map.shard_map( - pipeline_shard_fn, - mesh, - in_shardings, - out_shardings, + devices = jax.devices() + num_devices = len(devices) + self.logger.info("Number of devices: %d", num_devices) + + mesh = Mesh(devices, ("data",)) + + # — sharding specs by rank — + replicate_0d = NamedSharding(mesh, P()) # for scalars + replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays + shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) + replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube + + # — 1) allocate empty instances — + galaxy_spec = object.__new__(Galaxy) + stars_spec = object.__new__(StarsData) + gas_spec = object.__new__(GasData) + rubix_spec = object.__new__(RubixData) + + # — 2) assign NamedSharding to each field — + # galaxy + galaxy_spec.redshift = replicate_0d + galaxy_spec.center = replicate_1d + galaxy_spec.halfmassrad_stars = replicate_0d + + # stars + stars_spec.coords = shard_2d + stars_spec.velocity = shard_2d + stars_spec.mass = replicate_1d + stars_spec.age = replicate_1d + stars_spec.metallicity = replicate_1d + stars_spec.pixel_assignment = replicate_1d + stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + stars_spec.mask = replicate_1d + stars_spec.spectra = shard_2d + stars_spec.datacube = replicate_3d + + # gas (same idea) + gas_spec.coords = shard_2d + gas_spec.velocity = shard_2d + gas_spec.mass = replicate_1d + gas_spec.density = replicate_1d + gas_spec.internal_energy = replicate_1d + gas_spec.metallicity = replicate_1d + gas_spec.metals = replicate_1d + gas_spec.sfr = replicate_1d + gas_spec.electron_abundance = replicate_1d + gas_spec.pixel_assignment = replicate_1d + gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + gas_spec.mask = replicate_1d + gas_spec.spectra = shard_2d + gas_spec.datacube = replicate_3d + + # — link them up — + rubix_spec.galaxy = galaxy_spec + rubix_spec.stars = stars_spec + rubix_spec.gas = gas_spec + + + @partial(jax.jit, + #how inputs ARE sharded when the function is called + in_shardings = (rubix_spec,), + out_shardings = replicate_3d, ) + def shard_pipeline(sharded_rubixdata): + out_local = self.func(sharded_rubixdata) + # locally computed partial cube + local_cube = out_local.stars.datacube + # reduce across devices + return local_cube with mesh: - sharded_datacubes = sharded_pipeline_fn(sharded_input_dict) + # `shard_pipeline` returns a GDA with shape (num_devices, 25,25,5994) + partial_cubes = shard_pipeline(inputdata) + # now in host‐land you can simply + #full_cube = jnp.sum(partial_cubes, axis=0) - # Combine the datacubes (here, by summing over the shard axis). - final_datacube = jnp.sum(sharded_datacubes, axis=0) - time_end = time.time() - self.logger.info( - "Sharded pipeline run completed in %.2f seconds.", time_end - time_start + return partial_cubes + + """ + def _shard_pipeline(sharded_rubixdata): + out_local = self.func(sharded_rubixdata) + local_cube = out_local.stars.datacube + # this requires that you actually be in a mesh context with an axis_name="data" + full_cube = lax.psum(local_cube, axis_name="data") + return full_cube + + # compile it + shard_pipeline = pjit( + _shard_pipeline, # <— the function + in_shardings = (rubix_spec,), + out_shardings = (replicate_3d,), ) + + # then inside your mesh: + with mesh: + final_datacube = shard_pipeline(inputdata) + return final_datacube + """ def gradient(self): """ From 249eb805a41417680ec7b780240109383901911e Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 23 Apr 2025 14:01:05 +0200 Subject: [PATCH 07/76] use multiple cpus --- ...x_pipeline_single_function_shard_map.ipynb | 57 ++++++++++++++++--- 1 file changed, 49 insertions(+), 8 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 2c6fd13f..001a7ae2 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,7 +2,48 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "import os\n", + "os.environ[\"OMP_NUM_THREADS\"] = \"2\"\n", + "os.environ[\"MKL_NUM_THREADS\"] = \"2\"\n", + "\n", + "# Tell XLA’s CPU backend to only expose 2 logical devices,\n", + "# and to use at most 2 threads per device\n", + "os.environ[\"XLA_FLAGS\"] = (\n", + " \"--xla_force_host_platform_device_count=2 \"\n", + " \"--xla_cpu_multi_thread_eigen_thread_pool_size=2\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "print(jax.devices())\n", + "# You should now see two `cpu` devices listed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -237,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -308,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -373,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -402,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -423,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -475,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { From d0b5d77044e795194612ae1fb4160fe4a646b9c5 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 23 Apr 2025 15:18:02 +0200 Subject: [PATCH 08/76] works on multiple cpus, and olso forparticle number not modulodevides, pading the input data, only typechecking for RubixData class has to be commented outbecause it gets in conflict with NamedSharding, now test on single and multiple GPUs --- ...x_pipeline_single_function_shard_map.ipynb | 294 +++++++++--------- rubix/core/data.py | 82 ++--- rubix/core/pipeline.py | 20 +- 3 files changed, 212 insertions(+), 184 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 001a7ae2..7252552c 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,48 +2,59 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." + "name": "stdout", + "output_type": "stream", + "text": [ + "Logical cores: 8\n", + "multiprocessing.cpu_count(): 8\n" ] } ], "source": [ "import os\n", - "os.environ[\"OMP_NUM_THREADS\"] = \"2\"\n", - "os.environ[\"MKL_NUM_THREADS\"] = \"2\"\n", + "import multiprocessing\n", + "\n", + "# Logical cores (includes hyperthreads)\n", + "print(\"Logical cores:\", os.cpu_count())\n", + "\n", "\n", - "# Tell XLA’s CPU backend to only expose 2 logical devices,\n", - "# and to use at most 2 threads per device\n", - "os.environ[\"XLA_FLAGS\"] = (\n", - " \"--xla_force_host_platform_device_count=2 \"\n", - " \"--xla_cpu_multi_thread_eigen_thread_pool_size=2\"\n", - ")" + "# Total threads/cores via multiprocessing\n", + "print(\"multiprocessing.cpu_count():\", multiprocessing.cpu_count())\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2)]\n" + ] + } + ], "source": [ + "import os\n", + "\n", + "# Tell XLA to fake 2 host CPU devices\n", + "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", + "\n", "import jax\n", + "\n", + "# Now JAX will list two CpuDevice entries\n", "print(jax.devices())\n", - "# You should now see two `cpu` devices listed.\n" + "# → [CpuDevice(id=0), CpuDevice(id=1)]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -102,23 +113,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-23 13:26:24,875 - rubix - INFO - \n", + "2025-04-23 15:16:56,935 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-04-23 13:26:24,875 - rubix - INFO - Rubix version: 0.0.post400+gee789d5.d20250306\n", - "2025-04-23 13:26:24,875 - rubix - INFO - JAX version: 0.5.0\n", - "2025-04-23 13:26:24,875 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" + "2025-04-23 15:16:56,935 - rubix - INFO - Rubix version: 0.0.post400+gee789d5.d20250306\n", + "2025-04-23 15:16:56,936 - rubix - INFO - JAX version: 0.5.0\n", + "2025-04-23 15:16:56,936 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2)] devices\n" ] } ], @@ -278,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -286,62 +297,78 @@ "output_type": "stream", "text": [ "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-23 13:26:25,177 - rubix - INFO - Getting rubix data...\n", - "2025-04-23 13:26:25,178 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-23 13:26:25,237 - rubix - INFO - Centering stars particles\n", - "2025-04-23 13:26:25,720 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", - "2025-04-23 13:26:25,722 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", - "2025-04-23 13:26:25,722 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-23 13:26:25,722 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-23 13:26:25,723 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-23 13:26:25,724 - rubix - INFO - Calculating spatial bin edges...\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-23 15:16:57,259 - rubix - INFO - Getting rubix data...\n", + "2025-04-23 15:16:57,260 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-23 15:16:57,317 - rubix - INFO - Centering stars particles\n", + "2025-04-23 15:16:57,823 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", + "2025-04-23 15:16:57,824 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", + "2025-04-23 15:16:57,825 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-23 15:16:57,825 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-23 15:16:57,825 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-23 15:16:57,826 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:25,731 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 15:16:57,833 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:25,865 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-23 15:16:57,965 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:25,871 - rubix - INFO - Getting cosmology...\n", - "2025-04-23 13:26:25,893 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-23 13:26:25,907 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 15:16:57,971 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 15:16:58,005 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 15:16:58,020 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:25,942 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-23 15:16:58,055 - rubix - DEBUG - SSP Wave: (5994,)\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:25,949 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 15:16:58,062 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:26,099 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-23 13:26:26,100 - rubix - INFO - Compiling the expressions...\n", - "2025-04-23 13:26:26,100 - rubix - INFO - Running the pipeline on the input data...\n", - "2025-04-23 13:26:26,101 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-23 13:26:26,139 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-23 13:26:26,141 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-23 13:26:26,148 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-23 13:26:26,149 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-23 13:26:26,236 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-23 13:26:26,236 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-23 13:26:26,238 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-23 13:26:26,238 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-23 13:26:26,238 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-23 13:26:26,330 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-23 13:26:26,331 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-23 13:26:26,331 - rubix - INFO - Convolving with PSF...\n", - "2025-04-23 13:26:26,332 - rubix - INFO - Convolving with LSF...\n", - "2025-04-23 13:26:26,335 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", - "2025-04-23 13:26:28,030 - rubix - INFO - Pipeline run completed in 2.31 seconds.\n" + "2025-04-23 15:16:58,213 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-23 15:16:58,213 - rubix - INFO - Compiling the expressions...\n", + "2025-04-23 15:16:58,213 - rubix - INFO - Running the pipeline on the input data...\n", + "2025-04-23 15:16:58,214 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-23 15:16:58,251 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-23 15:16:58,254 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-23 15:16:58,261 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-23 15:16:58,261 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-23 15:16:58,347 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-23 15:16:58,348 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-23 15:16:58,350 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-23 15:16:58,350 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-23 15:16:58,350 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-23 15:16:58,376 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-23 15:16:58,377 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-23 15:16:58,377 - rubix - INFO - Convolving with PSF...\n", + "2025-04-23 15:16:58,378 - rubix - INFO - Convolving with LSF...\n", + "2025-04-23 15:16:58,380 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "2025-04-23 15:17:00,110 - rubix - INFO - Pipeline run completed in 2.28 seconds.\n" ] } ], "source": [ "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config)\n", "\n", "inputdata = pipe.prepare_data()\n", "rubixdata = pipe.run(inputdata)" @@ -349,89 +376,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-23 13:26:28,042 - rubix - INFO - Getting rubix data...\n", - "2025-04-23 13:26:28,044 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-23 13:26:28,065 - rubix - INFO - Centering stars particles\n", - "2025-04-23 13:26:28,273 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", - "2025-04-23 13:26:28,275 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", - "2025-04-23 13:26:28,275 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-23 13:26:28,275 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-23 13:26:28,276 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-23 13:26:28,277 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-23 15:17:00,122 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-23 15:17:00,123 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-23 15:17:00,124 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-23 15:17:00,126 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:28,286 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 15:17:00,139 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:28,294 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-23 15:17:00,149 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:28,301 - rubix - INFO - Getting cosmology...\n", - "2025-04-23 13:26:28,316 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-23 13:26:28,329 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 15:17:00,157 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 15:17:00,173 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-23 15:17:00,186 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:28,349 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-23 15:17:00,208 - rubix - DEBUG - SSP Wave: (5994,)\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:28,358 - rubix - INFO - Getting cosmology...\n", + "2025-04-23 15:17:00,216 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 13:26:28,372 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-23 13:26:28,373 - rubix - INFO - Compiling the expressions...\n", - "2025-04-23 13:26:28,385 - rubix - INFO - Number of devices: 1\n", - "2025-04-23 13:26:28,386 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-23 13:26:28,429 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-23 13:26:28,431 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-23 13:26:28,438 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-23 13:26:28,438 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-23 13:26:28,488 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-23 13:26:28,488 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-23 13:26:28,490 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-23 13:26:28,490 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-23 13:26:28,490 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-23 13:26:28,516 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-23 13:26:28,517 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-23 13:26:28,517 - rubix - INFO - Convolving with PSF...\n", - "2025-04-23 13:26:28,518 - rubix - INFO - Convolving with LSF...\n", - "2025-04-23 13:26:28,520 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n" + "2025-04-23 15:17:00,230 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-23 15:17:00,230 - rubix - INFO - Compiling the expressions...\n", + "2025-04-23 15:17:00,240 - rubix - INFO - Number of devices: 3\n", + "2025-04-23 15:17:00,274 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-23 15:17:00,315 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-23 15:17:00,318 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-23 15:17:00,324 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-23 15:17:00,325 - rubix - DEBUG - Input shapes: Metallicity: 1002, Age: 1002\n", + "2025-04-23 15:17:00,376 - rubix - DEBUG - Calculation Finished! Spectra shape: (1002, 5994)\n", + "2025-04-23 15:17:00,376 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-23 15:17:00,379 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-23 15:17:00,379 - rubix - DEBUG - Doppler Shifted SSP Wave: (1002, 5994)\n", + "2025-04-23 15:17:00,379 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-23 15:17:00,404 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-23 15:17:00,405 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-23 15:17:00,405 - rubix - INFO - Convolving with PSF...\n", + "2025-04-23 15:17:00,407 - rubix - INFO - Convolving with LSF...\n", + "2025-04-23 15:17:00,409 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "2025-04-23 15:17:01,669 - rubix - INFO - Pipeline run completed in 1.55 seconds.\n" ] } ], "source": [ - "inputdata = pipe.prepare_data()\n", + "#NBVAL_SKIP\n", + "\n", + "#inputdata = pipe.prepare_data()\n", "shard_rubixdata = pipe.run_sharded(inputdata)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(25, 25, 3721)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shard_rubixdata.shape" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -443,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -464,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -477,16 +482,16 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -516,24 +521,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -544,12 +539,27 @@ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", "visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", + "sharded_visible_spectra = shard_rubixdata[ :, :, visible_indices[0]]\n", "#visible_spectra.shape\n", "\n", - "# Sum up all spectra to create an image\n", - "image = jnp.sum(visible_spectra, axis = 2)\n", - "plt.imshow(image, origin=\"lower\", cmap=\"inferno\")\n", - "plt.colorbar()" + "image = jnp.sum(visible_spectra, axis=2)\n", + "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", + "\n", + "# Plot side by side\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# Original IFU datacube image\n", + "im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", + "axes[0].set_title(\"Original IFU Datacube\")\n", + "fig.colorbar(im0, ax=axes[0])\n", + "\n", + "# Sharded IFU datacube image\n", + "im1 = axes[1].imshow(sharded_image, origin=\"lower\", cmap=\"inferno\")\n", + "axes[1].set_title(\"Sharded IFU Datacube\")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { diff --git a/rubix/core/data.py b/rubix/core/data.py index 213a13a5..ae560007 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -81,18 +81,18 @@ class Galaxy: center: Optional[jnp.ndarray] = None halfmassrad_stars: Optional[jnp.ndarray] = None - #def __repr__(self): - # representationString = ["Galaxy:"] - # for k, v in self.__dict__.items(): - # if not k.endswith("_unit"): - # if v is not None: - # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - # attrString += f", unit = {getattr(self, k + '_unit')}" - # representationString.append(attrString) - # else: - # representationString.append(f"{k}: None") - # return "\n\t".join(representationString) + def __repr__(self): + representationString = ["Galaxy:"] + for k, v in self.__dict__.items(): + if not k.endswith("_unit"): + if v is not None: + attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + attrString += f", unit = {getattr(self, k + '_unit')}" + representationString.append(attrString) + else: + representationString.append(f"{k}: None") + return "\n\t".join(representationString) def tree_flatten(self): """ @@ -154,18 +154,18 @@ class StarsData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - #def __repr__(self): - # representationString = ["StarsData:"] - # for k, v in self.__dict__.items(): - # if not k.endswith("_unit"): - # if v is not None: - # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - # attrString += f", unit = {getattr(self, k + '_unit')}" - # representationString.append(attrString) - # else: - # representationString.append(f"{k}: None") - # return "\n\t".join(representationString) + def __repr__(self): + representationString = ["StarsData:"] + for k, v in self.__dict__.items(): + if not k.endswith("_unit"): + if v is not None: + attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + attrString += f", unit = {getattr(self, k + '_unit')}" + representationString.append(attrString) + else: + representationString.append(f"{k}: None") + return "\n\t".join(representationString) def tree_flatten(self): """ @@ -244,18 +244,18 @@ class GasData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - #def __repr__(self): - # representationString = ["GasData:"] - # for k, v in self.__dict__.items(): - # if not k.endswith("_unit"): - # if v is not None: - # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - # attrString += f", unit = {getattr(self, k + '_unit')}" - # representationString.append(attrString) - # else: - # representationString.append(f"{k}: None") - # return "\n\t".join(representationString) + def __repr__(self): + representationString = ["GasData:"] + for k, v in self.__dict__.items(): + if not k.endswith("_unit"): + if v is not None: + attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + attrString += f", unit = {getattr(self, k + '_unit')}" + representationString.append(attrString) + else: + representationString.append(f"{k}: None") + return "\n\t".join(representationString) def tree_flatten(self): """ @@ -317,11 +317,11 @@ class RubixData: stars: Optional[StarsData] = None gas: Optional[GasData] = None - #def __repr__(self): - # representationString = ["RubixData:"] - # for k, v in self.__dict__.items(): - # representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) - # return "\n\t".join(representationString) + def __repr__(self): + representationString = ["RubixData:"] + for k, v in self.__dict__.items(): + representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) + return "\n\t".join(representationString) # def __post_init__(self): # if self.stars is not None: diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index f4fa06b2..55f6f363 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -196,7 +196,7 @@ def run_sharded(self, inputdata): jax.numpy.ndarray The final datacube combined from all shards. """ - #time_start = time.time() + time_start = time.time() # Assemble and compile the pipeline as before. functions = self._get_pipeline_functions() self._pipeline = pipeline.LinearTransformerPipeline( @@ -264,12 +264,25 @@ def run_sharded(self, inputdata): rubix_spec.stars = stars_spec rubix_spec.gas = gas_spec + n = inputdata.stars.coords.shape[0] + pad = (3 - (n % 3)) % 3 + + if pad: + # pad along the first axis + inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0,pad),(0,0))) + inputdata.stars.velocity = jnp.pad(inputdata.stars.velocity, ((0,pad),(0,0))) + inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0,pad))) + inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0,pad))) + inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0,pad))) + + @partial(jax.jit, #how inputs ARE sharded when the function is called in_shardings = (rubix_spec,), out_shardings = replicate_3d, ) + def shard_pipeline(sharded_rubixdata): out_local = self.func(sharded_rubixdata) # locally computed partial cube @@ -283,6 +296,11 @@ def shard_pipeline(sharded_rubixdata): # now in host‐land you can simply #full_cube = jnp.sum(partial_cubes, axis=0) + time_end = time.time() + self.logger.info( + "Pipeline run completed in %.2f seconds.", time_end - time_start + ) + return partial_cubes """ From 3c7d2e62da52cd8cd530d4a7570de364fe47c19c Mon Sep 17 00:00:00 2001 From: anschaible Date: Thu, 24 Apr 2025 15:33:20 +0200 Subject: [PATCH 09/76] experiment with sharding, does not work for large particle sizes yet --- notebooks/rubix_pipeline_sharding.py | 0 ...x_pipeline_single_function_shard_map.ipynb | 258 +++++++++--------- rubix/core/data.py | 82 +++--- rubix/core/pipeline.py | 55 ++-- 4 files changed, 210 insertions(+), 185 deletions(-) create mode 100644 notebooks/rubix_pipeline_sharding.py diff --git a/notebooks/rubix_pipeline_sharding.py b/notebooks/rubix_pipeline_sharding.py new file mode 100644 index 00000000..e69de29b diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 7252552c..cfd45a32 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -9,8 +9,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Logical cores: 8\n", - "multiprocessing.cpu_count(): 8\n" + "Logical cores: 72\n", + "multiprocessing.cpu_count(): 72\n" ] } ], @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2)]\n" + "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)]\n" ] } ], @@ -45,6 +45,11 @@ "# Tell XLA to fake 2 host CPU devices\n", "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", + "# Only make GPU 0 and GPU 1 visible to JAX:\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '6,7,8,9'\n", + "\n", + "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", + "\n", "import jax\n", "\n", "# Now JAX will list two CpuDevice entries\n", @@ -62,7 +67,8 @@ "import os\n", "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'" + "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" ] }, { @@ -113,23 +119,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-23 15:16:56,935 - rubix - INFO - \n", + "2025-04-24 15:21:55,657 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-04-23 15:16:56,935 - rubix - INFO - Rubix version: 0.0.post400+gee789d5.d20250306\n", - "2025-04-23 15:16:56,936 - rubix - INFO - JAX version: 0.5.0\n", - "2025-04-23 15:16:56,936 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2)] devices\n" + "2025-04-24 15:21:55,658 - rubix - INFO - Rubix version: 0.0.post415+gd0b5d77\n", + "2025-04-24 15:21:55,659 - rubix - INFO - JAX version: 0.6.0\n", + "2025-04-24 15:21:55,659 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" ] } ], @@ -296,7 +302,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n" ] } @@ -308,132 +314,134 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-23 15:16:57,259 - rubix - INFO - Getting rubix data...\n", - "2025-04-23 15:16:57,260 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-23 15:16:57,317 - rubix - INFO - Centering stars particles\n", - "2025-04-23 15:16:57,823 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", - "2025-04-23 15:16:57,824 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", - "2025-04-23 15:16:57,825 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-23 15:16:57,825 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-23 15:16:57,825 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-23 15:16:57,826 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-23 15:16:57,833 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-23 15:16:57,965 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-23 15:16:57,971 - rubix - INFO - Getting cosmology...\n", - "2025-04-23 15:16:58,005 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-23 15:16:58,020 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-23 15:16:58,055 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-23 15:16:58,062 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-23 15:16:58,213 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-23 15:16:58,213 - rubix - INFO - Compiling the expressions...\n", - "2025-04-23 15:16:58,213 - rubix - INFO - Running the pipeline on the input data...\n", - "2025-04-23 15:16:58,214 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-23 15:16:58,251 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-23 15:16:58,254 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-23 15:16:58,261 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-23 15:16:58,261 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-23 15:16:58,347 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-23 15:16:58,348 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-23 15:16:58,350 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-23 15:16:58,350 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-23 15:16:58,350 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-23 15:16:58,376 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-23 15:16:58,377 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-23 15:16:58,377 - rubix - INFO - Convolving with PSF...\n", - "2025-04-23 15:16:58,378 - rubix - INFO - Convolving with LSF...\n", - "2025-04-23 15:16:58,380 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", - "2025-04-23 15:17:00,110 - rubix - INFO - Pipeline run completed in 2.28 seconds.\n" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." ] } ], "source": [ "#NBVAL_SKIP\n", "\n", - "inputdata = pipe.prepare_data()\n", - "rubixdata = pipe.run(inputdata)" + "#inputdata = pipe.prepare_data()\n", + "#rubixdata = pipe.run(inputdata)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-23 15:17:00,122 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-23 15:17:00,123 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-23 15:17:00,124 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-23 15:17:00,126 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-04-24 15:21:56,525 - rubix - INFO - Getting rubix data...\n", + "2025-04-24 15:21:56,527 - rubix - INFO - Loading data from IllustrisAPI\n", + "2025-04-24 15:21:56,528 - rubix - INFO - Reusing existing file galaxy-id-14.hdf5. If you want to download the data again, set reuse=False.\n", + "2025-04-24 15:21:56,559 - rubix - INFO - Loading data into input handler\n", + "2025-04-24 15:21:56,561 - rubix - DEBUG - Loading data from Illustris file..\n", + "2025-04-24 15:21:56,562 - rubix - DEBUG - Checking if the fields are present in the file...\n", + "2025-04-24 15:21:56,562 - rubix - DEBUG - Keys in the file: \n", + "2025-04-24 15:21:56,563 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", + "2025-04-24 15:21:56,564 - rubix - DEBUG - Matching fields: {'SubhaloData', 'Header', 'PartType4'}\n", + "2025-04-24 15:21:56,568 - rubix - DEBUG - Found 484076 valid particles out of 484076\n", + "2025-04-24 15:21:56,913 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", + "2025-04-24 15:22:01,771 - rubix - DEBUG - Converting to Rubix format..\n", + "2025-04-24 15:22:01,775 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", + "2025-04-24 15:22:01,776 - rubix - DEBUG - Keys in the particle data: dict_keys(['stars'])\n", + "2025-04-24 15:22:01,776 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", + "2025-04-24 15:22:01,776 - rubix - DEBUG - Matching fields: {'stars'}\n", + "2025-04-24 15:22:01,777 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", + "2025-04-24 15:22:01,777 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", + "2025-04-24 15:22:01,778 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-04-24 15:22:01,778 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", + "2025-04-24 15:22:01,786 - rubix - DEBUG - Converting redshift for galaxy data into \n", + "2025-04-24 15:22:01,788 - rubix - DEBUG - Converting center for galaxy data into kpc\n", + "2025-04-24 15:22:01,789 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", + "2025-04-24 15:22:01,790 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", + "2025-04-24 15:22:01,815 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", + "2025-04-24 15:22:01,822 - rubix - DEBUG - Converting metallicity for particle type stars into \n", + "2025-04-24 15:22:01,876 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", + "2025-04-24 15:22:01,885 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", + "2025-04-24 15:22:01,922 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-04-24 15:22:02,014 - rubix - INFO - Centering stars particles\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted to Rubix format!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-24 15:22:04,139 - rubix - WARNING - The Subset value is set in config. Using only subset of size 10000 for stars\n", + "2025-04-24 15:22:04,141 - rubix - INFO - Data loaded with 10000 star particles and 0 gas particles.\n", + "2025-04-24 15:22:04,142 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-24 15:22:04,142 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-24 15:22:04,143 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-24 15:22:04,145 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 15:17:00,139 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-04-24 15:22:04,163 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 15:17:00,149 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-04-24 15:22:04,607 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 15:17:00,157 - rubix - INFO - Getting cosmology...\n", - "2025-04-23 15:17:00,173 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-23 15:17:00,186 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-04-24 15:22:04,626 - rubix - INFO - Getting cosmology...\n", + "2025-04-24 15:22:04,709 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-24 15:22:04,798 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 15:17:00,208 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-04-24 15:22:04,938 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 15:17:00,216 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-04-24 15:22:04,956 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-23 15:17:00,230 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-23 15:17:00,230 - rubix - INFO - Compiling the expressions...\n", - "2025-04-23 15:17:00,240 - rubix - INFO - Number of devices: 3\n", - "2025-04-23 15:17:00,274 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-23 15:17:00,315 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-23 15:17:00,318 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-23 15:17:00,324 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-23 15:17:00,325 - rubix - DEBUG - Input shapes: Metallicity: 1002, Age: 1002\n", - "2025-04-23 15:17:00,376 - rubix - DEBUG - Calculation Finished! Spectra shape: (1002, 5994)\n", - "2025-04-23 15:17:00,376 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-23 15:17:00,379 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-23 15:17:00,379 - rubix - DEBUG - Doppler Shifted SSP Wave: (1002, 5994)\n", - "2025-04-23 15:17:00,379 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-23 15:17:00,404 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-23 15:17:00,405 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-23 15:17:00,405 - rubix - INFO - Convolving with PSF...\n", - "2025-04-23 15:17:00,407 - rubix - INFO - Convolving with LSF...\n", - "2025-04-23 15:17:00,409 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", - "2025-04-23 15:17:01,669 - rubix - INFO - Pipeline run completed in 1.55 seconds.\n" + "2025-04-24 15:22:05,573 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-24 15:22:05,575 - rubix - INFO - Compiling the expressions...\n", + "2025-04-24 15:22:05,575 - rubix - INFO - Number of devices: 4\n", + "2025-04-24 15:22:05,578 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-24 15:22:05,687 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-24 15:22:05,693 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-24 15:22:05,722 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-24 15:22:05,723 - rubix - DEBUG - Input shapes: Metallicity: 10000, Age: 10000\n", + "2025-04-24 15:22:05,869 - rubix - DEBUG - Calculation Finished! Spectra shape: (10000, 5994)\n", + "2025-04-24 15:22:05,870 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-24 15:22:05,876 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-24 15:22:05,877 - rubix - DEBUG - Doppler Shifted SSP Wave: (10000, 5994)\n", + "2025-04-24 15:22:05,878 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-24 15:22:05,949 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-24 15:22:05,952 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-24 15:22:05,953 - rubix - INFO - Convolving with PSF...\n", + "2025-04-24 15:22:05,957 - rubix - INFO - Convolving with LSF...\n", + "2025-04-24 15:22:05,963 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "E0424 15:22:24.952429 3558774 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: INTERNAL: jaxlib/gpu/solver_handle_pool.cc:37: operation gpusolverDnCreate(&handle) failed: cuSolver internal error\n", + "E0424 15:22:24.957338 3558777 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: INTERNAL: jaxlib/gpu/solver_handle_pool.cc:37: operation gpusolverDnCreate(&handle) failed: cuSolver internal error\n" ] } ], "source": [ "#NBVAL_SKIP\n", "\n", - "#inputdata = pipe.prepare_data()\n", + "inputdata = pipe.prepare_data()\n", "shard_rubixdata = pipe.run_sharded(inputdata)" ] }, @@ -448,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -469,20 +477,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(25, 25, 3721)\n" - ] - }, { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 9, @@ -491,7 +492,7 @@ }, { "data": { - "image/png": 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", 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wJXSt+nXJVsR/lYTcLofrfB1SdFmFMR9EYtXuY3h79d5kD8MQX3NANyPkqU82w0qs5zFVPxTpGDHq+nrDaT1w3dgeuu+nupU/Py23i7weRyThAQApbpdim/RUD1ItxJIkC9FMrW6QR9oGW0J1OaK1egBB65wYdLphf8eL+xDdUwNV16ooOw1uV7CuD7/nFB+OZOP+Gkx/Y01Yf4Ups5di+htrpUJVbRFfi19XfLy0dLelVFW9lDe72S5qHr14KDpp1NkAglYPdWVILcuHKCTMlnxP8bgVlpv0FLfl+ijqxnqE7DgcFB99Y2SlGN4tJD72td17jV30hFqKxy2VWa+k64Xiw4n84P++wNur92Hq3NaqoPIJqy1X4PvDvO8NYx6s9GVp0anFEamsuVk8LpduSnCqhiDQivlobPajsdmPD9eF1/TQQm75SHG7kOJxmw5kFXknlEoZa1r8gbAy8FrEs7cOscfOI0HLVO/OWTHZ3/BuwYDt9Xs7ovgIT7MVKWHGiwTFhwMRJ8TNsjx7X4ye9uPNsh3HDFvA6wkKzXV1GtLtr2oNWtXrQPnNzmPSTUYLtwtwu12agbGAtotFz+3yl4Vb8cB/1ugeS876fdWSkEwPdcq1ksILAL0KMy2tbwZ/QMDkZ77ApGeWRLROnfmHzwxTqUniEcVH36LYiI9hIfGxYX91h+ry2tjsx66j4ZkuIlLQKcUHA047InOX78HBmkZMO2+A4Xry37w8ziGaUuHJxkqHW73z3Hv8RGslVZ1LcdVzSw33Ldbb0KvEqm35CF/W2OzH3xdvNzyWmo2h+glWRYdInU/ZlM7X4kf1iWYpVdAOFccasPVQ0Bx9tM4nlZrW4mCND3VNLVEHNpLY0OwPYM+xoIs2VpaPAV1ykJbiRm1jC3YfbUCvGImaZLPjcD0CQrC4X2eNQO+uecGuvjuPdPzGepGg5aMD8st31uOZT7da8qcqG421X/Fhpay5npWkzteCR97dgMuf/QrHGrSDUiOREspk0bV8aIiPSAGnZhEnCtHyYXUfavFxzfPLcOoTn2JX6OnXDg2yMvQzP/4eR0MBd9UnmvHXT7eGrZ+ojCMSmcrqRrQEBHhT3OgShQCVk+pxY3BJ0DKwvgPFfchdLlqVjk8OlaP/atuRMItPQ1ML/vnFDkz48yLc99q37fo+bAaKjw6G/AttNqhp9qLtmPPVLul1oA2YQe2aYq24XdQ/7sKsNKkc+avL92D1niq89PUuW+MQ0+j0zkNLaGjFfPhsTMJiILE3NXgMuRvl5vKeEbevbVSKD7GM+/tr7feEaZRZ1t75dh/uf30NAOA3//sOTy3YErb+CY2eOSQ5iFlhhVlpYUHS0SC5Xjqg+NDLCjqrf2ekp7qx+WAtfvHf9fhufzVW7T6GP8z7Hmf+4XP87sNN2H64Hv9buz9usVdtBbpdOhDN/oDiSVuvHoacPUcb8Id53yuWtQXFbWcIJ/fIR7OG20Uv/VbtoklLcSMNyoDTBpuTYEqEm7SW5UMUC3IaI1gtCrLSwlKGX1m2BwCQHhIzhdleLJx+NrK8HlMCovpEM3wtfny97SjG9GrtWurx2J941JaML7cdAQAs3nJYc/1mf0CzFsK+qhN47H/fYcro7rhgaInt8RDziNa/TlmxrZYrBZ12QPExoFg7K6ggKw2PXDQEj7y7Af9ZWYH/rKxQvN+zMBOCELRe/nvpLlwxunvcx5wsKD46CJ98V4kfvbIKv71kqLQs0gQoAKg6Ee5WaAuWDzsCyB8QorJ8pKW44VEpFbvXwiPGfOhlu6SEfzZagiSS5eOMfkW6gkIuZvqFboZdDGItRKoamvHvr3fjiY824Yx+hdLyHK/+7eJInQ+56am6mTVarp9nF23TzWyZ+NQSDOySgw/vO1MqsgYAf/j4e3yy8SC2Haqj+EgQVaL4iHGpfrnlQ4yxau9sDNUtGVKq3X4BAG44rSd6FmbiiQ83obKmEVlpKRhUkoMrRnfHeUO6oPpEM8pnfoZ1e6uxfm+1biuH9g7FRwfhrpdXAQB+9d530rKIJlJBO+jSygQeL+xM+s1+wVJTN/W5e1PcUqCoiF0dJgo/PQ0VK8uHukqqnHQNN85Fw7viX1/twtqKKt3tqk804aWluwAAX23T7iEjZ+/xBpz5h88xsiwf7009Q3MdLRH1x3mbDfe7+WAtdh9rUNSWEGvQ7Igi/oRY41h90BIYa8vHgC45SPW4UNPYgr3HT6CsIPZZVonkeH0T9odc3YO6GhdjO6t/Z8x7oLPme4XZXpw/tAs+WHcA763Z12HFB2M+OjBuE08SWhYGM+6aeLG5shbbDtXaEh/+QADNGuejdx3UIistxY2MNOWE3WSz5oToLvDrZN9EE/PxxytGSH8biYh0DTGT4nHjqatG6m4DBC0fWrVe9AJXF2w8GDaWb3Yewx0vrURFKPjVbuG2E01+BAIClmw5jDUVVSzOlASO14uWj9hmH6WluNGvODhJb+wAHW6XbA26EPt0zoo6U+vikaUAgA/XH+iwaecUHx0ErSdgM19arTk+WV/2Ol8LLvjLEkx8agmaW6yPocUvoFljglRfmUBAwMpdx8JcTmkeNzJSlQKgXpX5YRbx88jTMVVr3ZzkqbGZIRGktnw8NGkQrhpTJo3zyjFlumNIT9VuVKd2LampaWzWFF16mURy0SR+d656bikWbjqIK/++FD97cy22HrJXNdXX4sd/V+/FTf/6BpfN+gr1DERNOMdDbpd4dEgeWhqsdPpdByiz/m4oQPQHw7tGva+zB3RGtjcFB6obsamy/V8bLeh26SCkuN1o9itvzJHiJgQI0PLMJMPysedoA56RpVz6/NYnmeZAQLfOR72vBYdqfXhjZQVmLwrWzVCfe1qKO2zCtlttU7R83HFWb3y75zi+2HoEY3p2wsrdxwEA2enhPz25+MjLSEVDkx8nmpTiJ9sbHN+mxydh7/EGlOZlSJ121eiKjwjuOD23m961kGuZxhY/MtNaz62yphFvrbLfL8jXEsD/osiyIdFzImR9y0qL3HXZKkNLc/HWKmBjG27pYIYD1Sek4OlLTuoW9f7SUz0Y0T0PX28/ig37qjHUIIakvULx0UHQmk8iuS4EQTsmIRnZLpOfWaJ4qrUVcOoXNJ/OWwIChj46P2y5+hAZqZ5wt4sNCwzQWmQsNz0VL98+FkCwK60oPrQKgMldMfmZaThQ3YgjdUrrjHxi797J2EeuV2QsJULWil7xNTOl0Rua/JqxJnY5WNNoO+6mvfHWqr0ozUvH6f2Kkj0UBT5Zh+RYMyTUYG5jO7R8HK9vwt2vrMKB6kZkpnkQEICxvQuk4O5oGdZNFB81uPqUmOyyTUHx0UHQihQ3EzehtU4yxIfanG4n6LU5YC3gVE1RtjcsPiRay4cc+b7Vga3qZUXZQRO3OsYhyyDjRI1dy4ee203vWshFydXPLUVBDAMTp/1nrZSS2ZHZuL8GP3tzLQBg15MXJXk0SsTWC16d71M0DAm5XfZXN+J4fVPMg1rjyVur9mL5zmOKZTeaqKNjFtEltaGdW4X0YMxHB0HLjR9p3mwJCJoTTVuo82Fn0vcHhKhKwxfleMMsH3bFh5Z1QT7pa5VXl8ftFIZuwpU1avERPgFoBZYC2kGtgLbwkaPndtMLvj0hC4rdfrgeK3Yd1923HdO9Xg8dPZF0vL4J20Kl3NsLe4613ewdsfVCPCwfOemp6Bkqgtfe4j6W7wxmgon33rG9CzB5WPTxHiKi+NhcWdshg04pPjoIWs+yZmI3XtSo4NkW6nzYERHN/kBU4mPTgZowa4HdtGNNy4dCfIS/LxckBVnarbflbheRv107CucP6YJfXjhIsVzPTRIx5sOi28VKNVI7vUH0smy0CsoBwKWzvsLEpxZLzdDaA3JdF431Lh60ul1ib/kAgEGhMuubDRo1tjUCAUES2e/+5AysemQiXrvztIi/LSv0KsyCN8WNhiY/dh/reL1gKD46CFpuFzMlyj8JpUnK2V/ViCtmf4331iSmvK/WOO1YHFr8QlRWm8tHdQ/LdrGbaqtV4E2eZaJlGZEvEwNS5dVWAe2nz4lDuuD5m8aENbLSm7QjFZ/Te8rSE2Ly0umRyLbgNoqElhiqPtEs9bb5KlRFtT0gD5SOVNsl0cQz5gMA+ofSbduTtWrLoVpUn2hGZpoHQ0tzUZjtjWnpeSCYFj8wJMw2dYBUZDUUH+2EppYAvth6WLfhlrbbxd5E/OLXu7By93Gp/0a80SphbrfCaTTmyXH9i5CRpvxJWOmSK0c7piOS26V1mZjVorYqGD1ZDemqjI0Q40bU2LV86FnSGi1YPuIuPhpaxZqe26ktsmxHazG3Nmf5CN1ztIrgxQIxQHN7OxIfotVjVI9Oigq8sWZwSdD10hHFBwNO2wm//2iT5CLZ8fsLw1S2ptslzn7C7YfrkJuu3TraCuouqoC17rTSNoGArZ4wIqkadT7s1BsBtC0b7gjiQy5OxMBSteWlW6cM3WMOLMnB3DvH4uP1ldh0oAY3nqYd/GbXNKz3fTphofldhobbyC7itWls9uOnb67FWf2KMLIsX3rfTkfgZPHaN609PqJxHcaDpji7XUTxYbcWTDJYtSsYaDq6Z6cIa0bH4K4d1/JB8dFOkMdm1DW1hBWpspvtYpf9VScw4c+LAUQfna/uogrYe/oTBPuWCiAoGGJd50O5rPVvo7LoQLiFIMebgv/de2bEyomn9y3C6X2NUzUjuV300BIf1Sea8cZK83U8MqJ8ep48rARLthxGfZNfmhTnf1eJD9cdwIfrDmDqOX2ldc2kBrdFEh3wHQgIePjd9SjOSce08waEvR9vt0vfztlwuYDjDc04WudDYXZ0DzOJQLR8yBsvxoPBXUXLR/sRZmZpP3ZJIqGlKTSLhcXx3iu2WY8FWlVE311jr7BUNBNOqju8vLqZHiJaQkJrgo+UaitHbYEpK8hE7yLrwZpauFwu/OjsPvjBCGuR+VqT4p8/Me7PokZ9XlZxu1ySO0X8rOVdfWd9vl3622chFqUtkWjLx6bKGrz2TQWe+XQrahubw96Xsl3i5HbJSPOgW37QotfW4z58LX7sOlKPfVUn4HYBJ/eIr/gYFBIf+6pOKFyKHQGKj3aIdiBpYi0fVkztdvb12jd7bO0rGlO72+2yNTlqTcralg/jbBf5pKMWQZEKg1llxuTB+Nt1o/DED4eZ3kbrPP+9dLel40btdnG1xnKIn7VWzBDQWp+iPZAv65uS6JiP4/XNYX+v3nMcX4cCdqU6H3FyuwCtrpdth9uu+Hh1+W4Mf/QTjP9/iwAAY3oWxDSGSYu8jFRJmHW0MusUH22M5xZvx4Nvrg0TGFeO7i79rfVgpBVwGk/xEct9x9LMHO3TrnrSN8NVGv1VtCwbymyX8PflLh719rFM4ZNz/dieUhOrSMTiiVxP3L14q7kSjoO65LRaPmQxH1q0p5gPeQxQoi0f8pirmsZmtPgDuPzZr3HdP5fjaJ0v7m4XAOgvxn0cbJviY01FFR597zvpO+dyAbed2Sshx251vVB8kBix6UBNWCrlzI+/x5ur9mLZDmXlvEzZpKie+I/U+TStIfH0HcunwmiPY2Z7s3EK0U44diwfl5xUGtZKPlKRMa3zyctI1VxXb/1YYSYlG4iN4FRnE4l0yU2X/e3VLEb2k/F9cee4PkjzKN0uR+rCO/DK328PyAVUomM+5G7POl+LIobnQHWjNOHGU3xIGS9t0PLR1BLA9P+sQUtAwKShJVj5yER89dC5mBTDgmJGiFVgV+w6FmHN9gXFR5JYuesYJj/zBc4NmfDUqDNA5GmOayuqMOkvS/D594ewes9xjPndwrAeIECcxYfsKd5uUKaImWJoZt0OZiYco6auWkW81PzxihG4SNa5MtXjxsiyfJTmtU6gkdwuWudzZr8i/OjsPnjmmpPCYnjiZfkAALPfklg8kcsFlhx5oO9jlwzDOyoxV5Kbjp9PGoT0VI9k+VsV6pOzR6cAU3uK+ZC7iOwWtrNLvax5YV1jC375znrptfzhKB7l1UUkt0sbjPl4dflu7DhSj6JsL/5wxQgUZXtRmq+fdRZrLhjaBQCwcOMhVDW03udX7zmO332wERXttABZVOLjySefhMvlwgMPPCAta2xsxNSpU1FYWIjs7GxMmTIFBw+GF7LqiDS1BLB8x1FdM7CcBZuC1+RofZNm1061cJDP77e/tBLfV9bi9pdW4F9f7tQ9RqIKlUYtPkzcbCMFaIqYsXwYtZTvV5yNsb0LDLfvlp+hKHMuWiXk7e01A07dxgGnLpcLMyYPxqUndQtLpTZ7/rYw+T1R11Cx87kXZmlnMsgtTiluFwZ0ycHlJ7d2B5WLL7Fy6Z/mB4Nd63xt3+0iCAIOVJ+AIAj4fPMhLJAV9wsEBEVKdTQZW3aQP+i8/e1exWdRIxcfcbR8lBUES6wfrGlsc3VO3vk2WGzxvgn9dMVzPBlamofBXXPR5A/g7dXBsby/dj+u/PtS/PPLnfjDvNau1nOX78FH6w8kfIx2sP1tWrFiBZ577jmMGDFCsXzatGl4//338eabb2Lx4sXYv38/Lr/88qgH2h546etduPr5ZXjk3Q0R15VnPtz32rcKRQuEm7i1imcFBGNzvBmLgl3kpvpoH4jNjNPsg78Zy4dRJUKP24X//Kgcf79htOE6HpkYEP318iBST6SYjwgnpBZI8bV8mPsAWwIBfLBuP6a/sQaNzX7sPmr9iSvF48JZ/cNTgeWXS7QKya1rRqnJDRrZUkAwHVzEHxDwt8+24sN1ybkxP/HhJpTP/AxvrtyLW+eswJ3/XomtoXLi6jLxiY75kLtdPlpfqejKWhPKfnG74uv6K8zywuN2ISBA04qbLPZXncC6vdVwuYALhyfGzaLFdWN7AABmL96OR9/bgHtf+1Z6QF26/SgEQcD3lTX45Tvr8ZNXVyu++20VW+Kjrq4O119/Pf7xj3+gU6fWVKPq6mq88MILeOqpp3Duuedi9OjRmDNnDr7++mssW7YsZoNuq/x9cTDN761VkeseqH/HB1Q9PNQ3IL0J2ug25Q8I2HG4DruPKtNFYzGRyYdjNmZADzNVSc2WLjZjajeyfEjrGBwv1eNS3IjFyVIeRKpZXt3t0vzbzPHjG/Nhbr1AALhn7rd4e/U+vPT1LizafMjysbSKqwFqYRZcR37KRtdLK1UbAL7eHrRCTp27Gjf9azn+3ydbMHXuastjjpZ6Xwv+GbJQPvJe64PJxlAAobqgXuJjPpS/GbkbRqzB403xaNYSihUetwvFoWKFB1XNFJPJwpCFekzPTihKYv2RK0Z1R0FWGg7X+vBSKMPsxtN6wu0KWs8raxqxubK1Fsi6vVVJGql5bImPqVOn4qKLLsLEiRMVy1etWoXm5mbF8kGDBqFHjx5YunSp5r58Ph9qamoU/5yAegJUF9pST8hW+20AQXPquX9ejLP/tEhhyjQz+UZCbpmJ9mZp5knP7JjNWD7MTORGFZM9brciZiM9lIKYEkFcuCP0dpGjPt14Wj7kn6XRYeTugF1H6225NdTX/p2fnI6vfnGu4vxEK4gyQLf1A/ntpUMBBIOwaxqbsb9af7Ia9Kt5+HDdAXy1rbV8uZnv6/eVNVi6/WjE9dS8t2ZfmHXlXVmPJPn3syb0m29WXcdo3ZhWUceXyUv6i26XeNX4kFMcCjpuS+Ljk++C4uO8IV2SOo6MNA9+MXmQJNJeuHkMHr9smFT7Z9uhOmw/3PqQubEdFCWz/I16/fXXsXr1asycOTPsvcrKSqSlpSE/P1+xvEuXLqisrNTc38yZM5GXlyf9KysLT1tsL1jxiaqfIn7y6mqFBUFtCtezfBjdqGYvai24JK+lEc1E5mvxQxCUreujfVAzk0Vh9qnLTCM4M1YUt8HxUtxKy4cYLKns3RLJ8mH8XQmzfMS4zocc+eU3Om+5zvW1BBSThLysuRHqFOOTe3RCt/wMxecrjkG+TH49zuhXJC1771vrzQ+vfX4ZXlmmX5/EHxBwxeyluPYfyyTXiBmO1vlw/+trMHXuamkCb/YHkKrzWYsWmypVxlsys10AZZBpjWT5iL/46NLGLB+1jc1YvjMoQCcOTq74AIIp/Rt/ewGWzpiACaHx9OkcdJHtPFKPHbJMofYQhGrpG1VRUYH7778fr776KtLT0yNvYIIZM2agurpa+ldRURF5ozaKujS3Eeqb/JE6X5jrRY7eDcnsU5LcQmJ3Iqs+0YxRv12AG15YrhhPtG4XMzdbLUvEvef2C1u2YV9ky5kZ8WW0jvqzSw89FconVs2YD7k4sRzzEb+bv/zqG4mPtRVV0t9LthzBltDE/OjFQzB1fF+drZSkelz4cWhdeX0RheUjNAb5JZJ/Z8WASF9zwFYPoG92HTOMyzpS55OsAZUWJsKK461+9kO1jXhu8XYMe3Q+Fm3Rdk/VNbYgEBAw/Y01iuXJrPMBKIu2iZaPRDTp6yJZPrRTpxPNF1uPoNkvoE9RljTJJxtvikfxW+kTsnzsOFyPHTLLR3sQH5bKs61atQqHDh3CqFGjpGV+vx9LlizB3/72N8yfPx9NTU2oqqpSWD8OHjyIkpISzX16vV54vW2/lr8Z5P7sY/VNKMjS7ioKaE+mRlVD9awDZm9US7YexqUndYu8ogGLNh9CfZMfX207inMGFkvL7Qa2NvsDuOb5ZVLKpBFak6LdDBC3y4Wnrx6Jaf9Zq7uOkZsnLcWluO6i6JRbO6KN+VBbeuJo+FBYPsx65I7U+aT6GkXZXs00zFN7FeCSk0rxh4+/R21ogktxu3F63yKsfGQiCmW/D4/C8hFapnO9RPHR5A9E5Q4QBCHsOtf7WvDf1a0xW+JE7A8IuOvfK+FyAf+4aYymJU4eW1VzogUzPw5mIXy0Xtvqe7jWhzP+8FnYQ4eZ7K9ouXXONzjW0Iyhpbn4YusR3fVEK0h6HKubinTJDc4DVgRfPKj3teC1b/Zgzle7AAATBhcbb5BE+nQOio/th+ukLDBAP/28LWFJfEyYMAHr169XLLv11lsxaNAgPPTQQygrK0Nqaio+/fRTTJkyBQCwefNm7NmzB+Xl5bEbdRslXVYY6WBNo6H40Lp5GZWDjtbycf/ra1rFh817m1xc+WPgdlm3t9qU8AB0xIfNGdnjBoZ3y1cse/Ly4YrXRm6eVI9bYUkSTdJyMaRZ58NCzId6c7MBt/aQx3xYP05hdho6ZYZ/128sD1ZPfXrBFiD0MCsKNHXwnjfFjd5FWahtbJEqOsrHInddyC2MWk0JzeJrCYRZK69+fqnCeiamze88UodPvw9aMI7WN4WNf93eKsz8qDXlUUylN+I/K7WtvPFKtRXdpf6AgM83HwagtGZpIWa7WLHq2kWM+ThUm1zLx8//u06K23G7gKtP6ZHU8RjRuyhokfl6+1HFPflQrQ+Nzf6EfG52sSQ+cnJyMGyYshdEVlYWCgsLpeW33347pk+fjoKCAuTm5uLee+9FeXk5TjvttNiNuo0yqEuO9GOOZAzQuskbxSvovWXnPmW3UqVcfMhFj5lsFS2s+La15kQ7EyUQFAFyy8SfrhihqNER3Lf+9hlpHsX5i0IlJYLlQ26oieRGUYsfu+dqBmXMh/XtO2WmoVun8KJLoqlebrLXKisPBMXVR/edBZerdaKTn7JczMnjD2pOaDfbykrzoD5ktRhamovv9oe74040hd+c1W47MUBUnhHy6rI9uH9if+m1IAi45G9fKbb766dbNcdlhni4XWobmzH5mS/gawngtTvN34sly0ciAk5DMR+H4mD5WL+3GnO+2onRvTrhulN76D5cHKtvkupkXHtqD1wyslSRetzWEC0f4r20T+csHK7xodbXgr3HG9CvOCeZwzMk5t+op59+Gj/4wQ8wZcoUjBs3DiUlJXj77bdjfZg2iXxSj/T0onUPNsrU0BMMduYkK7c2ubCQT6hyF1FACKb0zl2+By3+AE7I2p0bYWXsWimadtNPPR5XxGqjepaGk8ryUZyTrjlBKCwfGvuUT5qRxq5+N56GD2W2i/UDpXrcyNFosOXVEh8GJ5KR5lGIAT1LkdvtkiZDPcuHfD+n9NIuGrd+X7Xi+60losUHAnn66dMLt2Dv8Vaztl5jO7vEI+D0sllfYe/xEzhc69NNke6cE+7+rjkRPO9EPEGLMR+Ho7B87Dhch/fX7sd7a/ZJtS4EQcC0N9bg7W/34eF3NuBJWVEuNd/tr4YgAL0KMzHz8uEo71toeyyJoDArTdGUsE9RNrqHCrZVHGvbtT6ibsm3aNEixev09HTMmjULs2bNinbX7Q75PUP+ZLzlYC1+/d4GTJs4AGP7BL/MLo0utEY1KvRuSFYmi00HavDnTzZbuln6BQHu0FjlGTjyQL+AAJz758UAgk9Y/++TzcjLSMXKR84zfZxIRKoYmp7qRqPJLqYel0sxmWlZIfTmyF9fPASAdufRSJYPeVfQyG4X5fta35dYIf9m2RGzKW6X5pOkKDrkwlGvzocWboMYmYxUDxqbA5JbQI1c6Ol1Hr3pX99g6jl98bPzB8LlcqFR4/cnpsE2qGph7DxSj+6dgjd5vTojdolHeXV5GqaWFQgITmTqiV+8vonIdhEtH0frm9DUEjAV5CoIAnYdbcCmAzV4dfluRUp1VpoH7049A8fqmxRl259bvANjehZops+KzdvEfiptHZfLhb6dsyX39cCSbLhdwfOoON624z7i2w/YYcizPuST8x0vrcSeYw24+vll2PXkRQC0b/JywaI2dMSiqdf1/1yOY/XWqgf6AwK0Hnrklg25MHp1+R40+wUcqWsKphkaTDZW3DVaMRQpqiBEs+LD7VJZPjT2rWeWHdAlaMZs1hi7MuA0/LzlwZGR6pao345rdXW528WGiUUveFYrBdlKmrf8Gqg/o+C+m3XdLvIndaOJc9bn27Fo82Gkpbjxj5vGhL3vawlg3d6qsAaQ2w/VYXTPTshMSwnLFomWeJdXX7+vWnN5pkYzP/FBJZ59XUQ6ZaYhxR0M5j5S58Nr3+zBvA2V+Ou1J0txQHJa/AHc/tJKLN5yWFrmcbswsnseKkJWnt9+sFGKz7nmlDKkp3rw4te78O6afTriI5jBNbikfYgPAOinEB+5UuzgHhsViBMJxUcM8SvER+sNREuBaql6I1eFnuXDrIm2R0GmrQjof321E7npqbjhtJ6KSUpeYErQKTjW2Ow3FB9WsmS09uNWxAEEJyMz7DhSrxAHWvOhljhY+chE6Sn6hIb1SL5PLctGbnqreTRT52lcj3hWlzSbaquH3mcsTmaKwFELQcLyz0DL8gFou136FWcr+pNEyogRLQH//CK8T9Lbq/dJWStyfvP+Rny4/gDevPt0KR6kJDcdF43oihcM+i3pMaBLNnoUZGHhpoNSUOiJZr+u1cYKy3coi6VpdY51uYybKiYi28UdKqC1v7oRWw/V4f8+2wYAeGrBFk1h+MW2I5LwGNI1F+V9C3HrGb3QvVMm9hxtwMSnFisyea45tQeaWgJ48etd+GbnMc1sJ9HyoSV22ipi3AcQjG86HnrAbOuWD3a1jSFyHaCsg2G8Xc/CoPnWqGKknsgw+5Q0qMRe4NEf523GI+9ugK/Frzg/uVCSL5dPLkapw4A133Yky0dqirVJM9ITuNYkLM9wuHhksM+DvLiW0pUTvn16qgfP3zgaj186FN0idMVUHz6euS49ClrHYie2RDzXBdPGST0oACAzNTiZKSwYVtwuipgP5XaiZUPL7TLv/rMU52E2XmFNRXjm1WaDImMrdgXXFy0fWV6P7dic7p0ykRb6DvsDAu7890qc+sTCmBTcuvp5ZWsL8X4k1ogAgtahDA3Lh0giAk4BoHMo7kMel7J482FN69LiUMbONaeU4aP7z8KvfjBEcoX1KMzE7Wf1ltYd0CUbI7vnYXi3PKS4XThc6wurjNvUEpDcM4O6tt1ATTXjBxYjM82DU3sVoE9RFspCv+e2HvNB8RFD5K6RSG4SceK9aERX6YlYaU3Q37fWfqyMzQ5NLQGFhUNeC0EhtFTbGGFFfGhWDNWoimkWpStAq8Os8faXjuyGt+4ux6t3jJWWpUXo7QIA5w8twY3lvSKOT30+8cx2efCCQbhqTHe8dudptiws4rn275KDW07vJS0XJzMj94kRboPtxMlQDIgUcbmCQkX+zTIbr2CnuJU/IEgxH9neFN2sNDU3ntZT8Toj1SNZzlr8Aj77/hAamvym+kRpcbjWh6lzV2OJzCWh5h5ZkT5visfws/EmwPIBtMZ9LNrcOu4mfwBfaJyHeG7jB2rX4Zh6Tj+U5qXD5QKmnzcALpcLGWkeyarx7R6l2Nx+uA4tAQE56SkRHw7aEgNLcrD8lxMw986xcLlcKOskBpw2RF0AMp5QfMQQ+QcdKWhMnHflwY+KOArVl0ZvojbbX8MfEBSTo1WaWgK6WTJ6oiuSUSYay8f8B8ZpVsU0i9JKEf5+pP253S6M6VWgMItHqnBqhbCYjziaPvIyUvHHK0aivG+hPcuHjitFK4YgZgGnaaLbRWn50Nq/2Ymz0qDCsMjY3srMmRPNfikTJsubgia/uWBusT+NSO+iLGnyl7ts7QZ6Pvnx9/hw3QHc9K9vdNdJT/VI94ROmamGv8dEmfDFQmPygllAeN2UPUcbQu5TF87op52Rku1Nwfv3nolPp5+NScNaO9Ke3CMfALB6d5Vi/e8rQy6Xkty4ujnjQU56qnT/Ea0/tb6WsFiltgTFRwxRFt7S/iFvDPmXxWBLj9slFVBq0omjAJR9NeSYFh9CdCWSfSrLhxxlh1v5MY3FhZV6BvJ4im75GRhYkqOYkKzeK+QTklaJbjvaQdHpNkq1oM5uSdTN0I6FRdHnRnZdxbgLt8JCZW8s6usp7rteFXsjTqbytc1O4JHchABQqnoi3n6oDve/vgZAUHzoVRG+4TRloaqw3k7n9JW+z8cbWicMozgMI/ZVRRYLaR43Hr5oMDrnePHnq04yFB+J6rdSnKNs23HHmUHXyf/W7MePXl6JkY99grdW7cXiUMn60T07IUcWS6WmMNsbVhr9pJCrVO1m+z4UbNqeXC5aZKR5pLRptYhrS1B8xBD5b1dvYr3wr1/gSJ1PmpjdMsuHT5FBotq3nuXDxA1T3D4a8dHUEtA1KctFhlxvRHL1WMl2kVsqxPu2/MnYaE7zprilJyqgNU7j6jFl6FecjbMHdA7bRh1walStVkT+1B1tI7iwmI8EPYjJJ/xfTB6E8QM7458awX5y5CKwR2Em7jmnHx65aLD0+ciHbkVEyfWG2pKkl30hfsdH9yyQrRv9be7hCwfj2etHhcU+PPxua8XnbG8KTulVgPI+rU/iO35/Idb/5nw8fqmyOKOcvIxUZKalSN+ZY/Wt7h+7GtaMaElNcePm03vhm19OwOienQwfFn4xaZC9gVikWFVr5KbyXsjLSEVLQMD87w6i+kQzHn5nPV4ONQY8e2D4bzcSJ/foBADYsL9G8cC3MRRsOqgdZbroMTSUKrxur3ZmU1uA4iOG6GV9qNlxuF6amN2uVnO9MojTntvlkpGlmpOFPyBElavfEhB0b056IiOSuLBm+Wi9C4uHS1FYPtQxEq1/r330fHz50LnS6zNChYP+cMUILJg2TjMgUb2/1++KXBUyUp0PK4S7XRKjPuQBnCO75+PFW0/F2D7aRbpE1F1bf3bBQNxxVp+ox+I2sCRpFTUDgDtDx/3p+QPw0/MGYMG0cZpuF6tZJHeO64MLh3cNc13KK6JmeYPHefyyocjLSMW1p5bB7XYhJz3VUHSJ9w3Rune0rjUdXiul2wxGwaMiYhyVODa5q1gustJT3Tg91Ek43oiFxoCgKCsryMA1pwSrD5fkpiM3PQW+lgC2HAwGho4fYL3vSq/CTORnpqKpJSC5Wpr9ASzfeQwAMLxbXrSnkXRE6446rqUtwVTbGCIXCEbiw9fiV7ldQjEffmXVUDl6E/xRVd2Oe87thwFdcjBxcBcslPlJ/UJ0lo+AIOiKCUEn5iOS2yXabBcjc36qxy0JM7W48BiIFq39XT6qm1Tfw4i0WMZ8qN0uUe3NPPLUVTH7ItJEHbE2iE3hpMx2Ue6jk8oS9ZuLh2B493zpppvlTcG9E4Il0NXfQ7cLWPfo+Rj4q49NdcX9+w2tjTSNfkNZoevUrzgHa35tvsCeOALxeynvbaJVzM4MkbomA+HuqFN7F+DLbcHU1N9eMgw//+86AK39QxKBvMrqyLJ8uFwuPDRpEK4c0x2l+RmorG6UChoWZadhsA0Xicvlwsju+Vi85TD+/MkW+AOCdN656SntpsCYEZL4iNC7J5nQ8hFDzLhdgGADOfGe4nZrB5yqJ3qzE7V4w/7LNScpxxYQLAX7qfEHBN1zkt8fAyatP2belyMP5mx9UtQPODWaJMxMhfL9mQ3UjavlI66N5bQRn8RdLhfW/Po8RVpxIjDKdlFPnAeqGzG6ZydNkTqoJFcR4FmanwG322X6cxXN9IBxwKxcMLpc2lVfNQn9DMS0eXkRMLvVTut8kd2x6nO58bSeuPbUHvjfPWegRyj9H9AOHI4X8piaU3oGr7vb7UK/4hxkpqWgT+ds/OmKEejeKQOPXzrMdizUqaHA4cVbDkvCIy3FjUcuGmKpEF5b5eQeneB2AbuPNihaAbQlaPmIIcpMD/2bxh3/XikVhgk2OQt3u6jvOWYLcok/HPXTql8QosqY8Af0LR/ysvDyVWKZ7aL1JCc3LqhvQt4UN/QqNJi5YSnEh0mLkaJwWdQBp6rXSbgfysVUfmYaikzEvWhh91LIPwO1JUk9ceakG9/KbirvhfzMNPz10614+qqTAIiCNvjdHT+ws5TeeeHwEhRkpeGVZXsABM3/eseVY1Vw3nduP/z1s23405UjQ9trFB60afmo80XOclCfS6esNMwMdXdeLTPXJ1J8FGSl4ZbTe2HZjqO45lTtbrJXjikLawRplRtO64lPNh7EjkN1uOqUMlxzShl6FWVF9YDWlsjLSMVJZflYvacKX249onstkwnFRwyRi49fvL0e4wZ0DouOF9kR6rUQjPkQ3S7KbBdBEPDClzvRv0uO6eBMvbLdgYAQVdxAQNCP+fA1y8fdujyi28VCDrp88hG3chm4XYyeas1cB/kqZp+QzdT5MItaIMWzt4tZ7FYMsDtyo2wX9eurTExGl4wsxSUjS6XX8t9r/+JsSXyUFWQiP6NVaMnddkZC1GqQ8bTzBuDG8l6Sq2H8wM548etdinXsWz7Ci3J1zUvHAVk6sdG5yL/LGQluy/6bS4ZGXilK8jJS8d7UMzSrnHYUxg3ojNV7qrBk6+E2KT46hsxrI6if9J/4aFPEbdzuVsuHfBL/3Yeb8NsPNuJ3H27Czf/6xvRELX94Ep9iANHyYf9H5g+VfNZC2ZDLvNvFSsCpvMiYeCnk56M+tVSDG6sZXSA3vRrtS44YcAhE35lUPcZkWILVE1+iCxYps12UF0D9hGq1XD2gtE5eLBMl+RlpaNTJIjMSoiO751s6vsvlUsQ4qNNMAft9XjZXhtv91CXKjc5F7tZKpOUj0XRU4QEAZ/UPZgJ9ufVIXDolRwvFRwxRB4U2mGg45XG5pInVpzKxzvlql/S32Scg+YR87ak98NRVQZOuPxCd6d5IfMhFk1xQRJqs/BFMyiWyyHf55CN215XPR2ExHwY31nwT7gN7MR+t68n7LdhBbelIVLaLnMJs5XVK9O3LqPmfuuKtnUwuuaDvLSs1nupx6f5W9HrTPDQpmJZsBr19a5Uwt+N2OVTbKAXSzr1jLC4a3hX//fHpCqEDRLB8yN7LsFlrhCSXkd3zkJuegprGFizfGezv8/yS7Sif+alUbyqZ8FsVQ9Tiw8yEoRdwqkbvSUyN+glRfJoKBASkRJHtYtbyIRci4vq7jtTj9RUVuP3M3uic40UgIODjDZU4XGdcztoo4DD4vn7WitaNdeo5ffHut/sxaWiJ4XHVx7aSJfT5z8bjeEOTVGXQNqrTTcYDmvoc7Bo+7MYtyD9TdRVVudBzu+y5ueRfZ3mhqrKCTJzaqwAfrjugcNMExxH+Xfj2V+eFZd8Yoeca1apdYsftsqWytXHcaX0KpTRZdTNEo/iGNIdYPjoyKR43LhpRite+2YOnPtmC/EvT8PuPgk0SZy3ahlnXjYqwhziPL6lH7wC0+AP4ZtcxnFSWD/XcbGbCcOsEnKqJ1CdFvj/F69A9RCvg9Cfj++LZRdtN7dcvCLrpvnLBIZ9oxCfLy579ClUNzdhxuA7P3zQGi7cextS5qyMeUxHToVHnw6WwfCi31bqxPnjBIDx4gbliSS4blg8g+ATdG9FZPYLH1x9PsrBr+SjJTceGfTUoyrYWsKpwfamCMeWftzfFY+v6qC1zr94xFku2HMa5g4qR6nFjwfSzI24DhKf9RkIvGFnLetMcQbj5AwJqTjQrxiAGm47u2UlxrIw0D7rlZ2BfVbDhmNmYj1yDCqKkbXPvuf3w3pp9WLn7OC786xfS8u2HwjsbJxq6XaLk2UXbcd0/luNHL68Km5zN3BA97lZTrqH4MPn0qJ4oxacsrYBVK6b8QEA/RkNu+VCUmA8NuSpULlrMOZ+3vtLUMZXiQu52CV+mPpcfj++Lwqw0qTyzVeTzg5U28LEivLFcwocQht1mW7+5ZCgmDCrGzMtHWNpObiFQW/Tkvy27FUzVlrwz+hVhxoWDDS0CdgNA5ehaPjTFh/Hx7vr3Spz8+AKpGyvQmmabpREHI/Y1ASLEfMisMNkRMolI26U0PwO/mBz+wLX1UJ3pB9p4QfERJa+Eyvx+sfVIuOXDxPbB8uohy4eBwDBTDAkIf5oRb9p+QQh7crWSDuoXDNwuzdrjVgfJiumyZo8bqSeI0fsluelY8fBEPPKDIaaOZbRvLVN7vAlLtU1wtovW9Z5x4SBcMbp7WOBiJLp3ysQLt5yC84Z0sbSdohuuRy3GZOLDpjvRTgyePAD0D1OG4+XbT7W8D706ElqVWCMVGfv0+2CPk1eX75aWHQ8VHtSqAit3TxmJ6nR5zEeCs11IbLlhbE9cP7YHCrLS8PcbRiMzzQN/QEhYs0A9KGmjRP5jVlsXzLpdxAc3K0o0I9Wj2QhLLT7EiV5LOOg9gWkRMFnnQ72NnNZ+NuaOqRdQquV2UV87t8sVVa0No9LeiSCRXW210Jogc9NT8f9CNSkSPQb1eNRul0QhT52/+hR76Yt67g4tMRDJ7SLSICsqJmbZaf285SnrRpZZeUxNNJWRSfJxu1144ofD8cQPg9mPz3yahU0HarDrSD36dk5c9dqwcSXtyB0EeSCcnYBTj7vV8qE3iWsx/bwBuvtTvDZ0u5g+nGFvF5+O5UOvP41Zd498Pa3y5vLdy1tmA/a60iqP3fp3UsSHOtslwWNIRnaN0RjUn0EsLB92mDysK245vRf+eu3JlrcdGPoOXzG6u+b7WmLAbG+X+qZgZp38IWPLwfB0WztGvJPK2n+vE9JKn1BmV7I73tLyESXyqoQNqmhyc5aP1puOFctHuskIdIXbRXUfM5rQUj0uhavHMNVWRzTprW96XnMB7049Ayt3HcMPT+6Gn765NvRGcL/y4L9rTinD6yv2YPfRoCkx2hLJ8sktGVUPkz33t4US00rrk/IzkF+fWHStNYvH7bJdBOvl20/Fkq1HcPHIrpFXDtFscE+Qf//FTBZRhADa31srPYeWPHgODtU2ol9x+24xT5T0Kgpmse06mlzxQctHDFErSVNlvN0uUwGnampORC6fDLROov5AuBgwerpVTz4BGzEf4dkxrojHVY/vpLJ83HFWH81sFzmpKW6MH9BaZ8GKS0nv2NK+2oTbxYmWj9a/jWM+2kdMQnFuOq4Y3d1wvBMHK+NijNwu8o7WouiokzUGfPjCwWHbWLHi9SjMxJhexh2NSfujR0FQfOw9fiKp46D4iBK99FPAXMCpvLeLz4L4OMXkTUGcOAMaqbJGD/Tq1Ea/kdtF1/KhfB1pPnvwgoE4VXZeevdJrVHILUhA9G4K+eknRXyEFRlL8PGTrz0UAlL9FJ8st0u8mXpOX8VrvQyzzzcfwojHPpFeN7UEsG5vFe577VsAwUwWsb6HnNwMGrudjih+k131tOP8apOEkfgwVWRMVuH0aChK3QxajbTO6h9+sxHv2VpuE6PxqZ80A4J+wOlHOqmzWq3MjXC5lGl9Vp6+3S6XMkAxytlTnoaY4KriALTqfCT2+G3N7aIWHwq3i03xMeeWU1CUnYY5t5xia/t4kKFyp4rW0Dpfi6Jk+jMLtyospXW+Flzyt6+wcnewIVzX/PBS7QBwc3kvjCzLxxM/HBbroZN2gvjbMZq7EgFlcJQYtV4wc/92ywJOrSBPf+tRkIlZ141C3+Lw4lbiBB7QsFwYi49wy4eVXixAsLKpvDKr+DSv96XPy0g1dc20Cj25XParkmoht6KEJynHH/VlSLQbJFrxFgsUAadhbpfWv+26Xc4ZVIwVD09sEwXcRNTX3dcSwE3/+gZLtgSb3r1026k4e0BnbNhXrVhvy0Fl0Si99Nji3HS8N/WMGI6YtDdc0pyQ3HHQ8hFHTBUZc9nLppB32hQgYHj3PGRq9GCQB5yqLRdGh1W3sPcbpNrq8dSCLfjB/30pvRYvh57gDmYByFwnEa7foJJc5KanYGCXHKR53MqqpDE0xSfDOqk+90RPkInOrtFCrn/VbkCj0utWaEvCAwgv6LXjcJ0kPADg7dV7AURuyvi9RmM5QoDW+34yHqrk0PIRJYYxHyYtH3ayKbSaUGnuXwo4Dbd8mM3z19veDPLKi+JY9L703hSPsraHzimK487LTMWyX05AijsoPOTiKBYZKmkpbjS1BDC6Z6eo92WVpNf5iPDl9bhdcfcZuwwtH7FzsbUluuZl4Pc/HI61FVX4z8oK1DQqm1PGosIqcTatFujkjoPiI0qMA07NxXyob6xmkJuajTSBIuBUZWYzmtDUYzKqcGoVo93olVTXQ27tkT8NxiIIceUjE1Hd0Gy7rHg0qIVhoqfXSDEfifAXKwNO9d0ubSE+JZZcN7YHehdl4T8rK8LeO1TbiNteXJGEUZGOgmT5YMxH+8bo8zNzT/S4XWE1DMxgdnKV3C5adTqMLB/qVFuDOh9mieR2AYw71UZCnpZopRmcHrnpqW2mqVai3SBaAc1yEnHfkosK9W9E+T2J/1gSTVqK9kmt2HXc1PZPXj48lsMhHQgp5iPJlg/GfESJ0edn5qbokWW7WMFqf5SAAFTWNCrHZ7Cd+mYfFC+Whqg7FqOrJr9mVq+KPPq/LcQsRIv8FBIVm3Bq72Cq803lvRJyPCOMertYtZC1N6J1G47onh+bgZAOh6uNWD4oPqLE6PMz43ZxuaI3G5txu2hhJdXWL0Rvajdj+VDU6rB4Wcz2wWgvyK9FoqbXF289Bf/98em45pQyw/WevX4UgGBztXhhts5HR4r5ELFjDZWTyKqvpH3hbiOWD7pdokb/E6xpbMbMjzcZbu2xGXBqFqMbs9E9W/1WwEaqrd4+zbpd9MSR3rDNdv5tL8jPM1FP95lpKaYCbC8c3hXfPz5JkXUVa4ya+ym+Jx1wntVzu5jePgktAUj7oK3EfPAbGiVa8/GgkmAvhI83VOK5xTsMtw/GfMRvYjG6MRsdVW3mbwkIeH/t/ujGIiluc1VhrU64VirEtgeUroXkjUOPeAoPwLi/TjJcUokky6t8Lrz1jF6Wtu9IVV9JbJEs0MkdBsVHtGipx3MGFZve3qWqzBlrjPZtdM9Wb7Zw08Gox2LmS684rsXL0tSR3S4db36NiPy7EGYdbOPCLFryM9IUr68cXWbpISWWdW5Ix8Jl4iEwEfAbGiVaH58Vk2cs/NVG5jMj64HRE6N6u1W7taPsf3eZ+TLN4vHiFvPR0Swf8r8dqD4U2S4OqfMhkpHmUVgvsr0pYQXIjGgvzfZI4mmtep3kcST38O0fzQ6rFrJXUjyuuJq/DC0fBttZ6TxrltaYD3PZLtbdLtoN7torHT2jIxJyl6a6wmk0KdnthdyM1jTv7PQUZHu1xYeYoSSHbheiR+Scw8TAb2iUaJmuPBYi4A5URd/W2OhLZBxwas8lo9i/hW+QuZiPyAGnenS4mA/ZteiY06t5jHq7dFRhJregZnk9moJi5uXDcfGIroplvQozO0SqOYkPUqVpul3aORYsH9eeGp6+qA4sszUEo+wRw1Rb/e3M3M/7FGVZeuoUVzVKSlEGEpreNQAgM61jmZrNlJrvyBTneKW/w7va2nfPtRfkVktvikfTinntqT0U12L1r87D/GnjEjI+0j4Rv0aM+WjnaH18eq4Ordz9i0eWIj3O/lm98UT7wPjSbafa8rcbNahTFBnTS7XVOeSTU0ZgaGku/n7DKMtjaosoJ9gOOsMaUJqfgZ9PGoiHLxwcFkDZkcuri6hPS21RveX0XgCAy07uhpHd8zD9vAEoyEpjvAcxxkS9pUTAOh9RojZdBVu764gPDYtIeqoHw7rlxmVsIh6XC34NmWSmCJrhft0uS0/k4nVpMYh0UqRXWpxU+nbOxof3nWVpm7ZMx5xSrfGT8f00lzsh5mPPsQbFa3m2y2c/PRs9C7MABINR37vnzISOjbRfzLi/EzKOpB69A6D++Dwul64ZWC9VzuVy4fJR3TTfM/NUF6k1sn53WIN9mvheul0uawGnotvFIDTDqJOpbC3Tx2zXOCCuwS5uB7hdfnbBQADA7Wf2BqC8F/TpnN1hLT4kvnjcLqR53HEtbmkGWj6iRK0e3W6X7qyublMvR8t9MW3iAKzdW4XPvj8U1Rj1XCPyif70voXoV5yNfy/dbXq/bgMrjxGGAaey3emXmE52nHZiiKbgWkfHCZlAd53VB6f1KcTwbnkA9B9eCLHCKb0KsOWJyckeBi0f0aKeR40sHy2qR/4R3fNat9PYqGt+OvabyIaJZKXQCzqVL7377L6mymortrdRIO1Inc9QTDnBl28W+efWQedX2yjERwf9nqR43BjVo5P0hOr03wPpWFB82ERsLx/mdnHruyKWbDmieJ2imFzCt/G4XKYqFUayA5gJOFWvY8a2EIxvMbFiCH9AwC/+u954nzJJpP+k54ybsNLykbRhtEmc4HZRM7IsP9lDICRm0O1igy+3HsFdL6/EY5cMDQs4dRtMyOoYBrkbRssj43ZH31ob0He7mGniZoTVmA9BADYdqImwz9a/nf6kpyyv7uxroSba72575JbTe2HXkXpcPqp7sodCSNRQfNjgF2+vQ0OTHw++tS5sgvS4XbpZJOo6FPJ6IFo3ULfLpVkIJsdCmWVxTFq4VOsoDmUq4NTajT8gCBErL8onWceLD52/ibrIWPLGkUhK8zPw/E1jkj0MQmICxYcNWmRVssItHy7T/vkrR7cWHdPaxO1yKUpMXzyyFN4UN6aeo0w/jBTzYc7tEjlrJnx7azEffkGI6EZygi/fLE6v82GE4to4/HtCSHvEkk1/9uzZGDFiBHJzc5Gbm4vy8nJ8/PHH0vvjx4+Hy+VS/Lv77rtjPuhk0yJTBOrp2m0Q83GSzGc755ZTcOlJpYbHcbuUfV/6F2fj/105Er2LsiyNV2886slNLmLMCBGDxB5thMjWDDMlxZ0yDyuFWPLG0RZxQnl1Qjoyliwf3bt3x5NPPon+/ftDEAS89NJLuPTSS/Htt99i6NChAIA777wTv/3tb6VtMjMzYzviNoA8VVQr20XvXnj/xAHI9qbi7IGdFUJED49beYCehXrX0lgomHG7pLjdGFJqrdiZVcuHgMgTBSeVVpRuF2dfCzVODDglpCNhSXxcfPHFitdPPPEEZs+ejWXLlkniIzMzEyUlJbEbYRvE6GZX52vRnTSzvSm4f2J/08dRBxlePELbUqJfDyOIvttFbroGBpXkYu4dY1GSl46fv7Uu4visxnwIghDRRC5/3+mTirLUfPLG0RZxYsApIR0J28Zcv9+P119/HfX19SgvL5eWv/rqqygqKsKwYcMwY8YMNDQ0GOwF8Pl8qKmpUfxr++jf7Op8LbYmCs0eMaod6U3cqSnmrQl6y0WBcnq/IvTpnG24v9btrWW7BITIgkLxtO/wSYUTrD5OKDJGSEfGcsDp+vXrUV5ejsbGRmRnZ+Odd97BkCFDAADXXXcdevbsidLSUqxbtw4PPfQQNm/ejLffflt3fzNnzsRjjz1m/wySQKQJNFY3Q7fbXL2Nomyv4fumAk5tjFld5+NPV4zA+IHFmPPVTjy7aHvY+gKEiMdRppdaHlKHQinEkjaMNgktZIS0byyLj4EDB2LNmjWorq7GW2+9hZtvvhmLFy/GkCFDcNddd0nrDR8+HF27dsWECROwfft29O3bV3N/M2bMwPTp06XXNTU1KCsLbz2fKLYerMXP3lqHByb2xzkDizXXiSQuYjVRRDrO328Yhb99vg1/vnKkrf3I4wjUVhVTRcagjPnwpnrQOcerK4YEwdq10x+3M2C2iz6KAmxUH4S0OyyLj7S0NPTrF0z1HD16NFasWIFnnnkGzz33XNi6Y8eOBQBs27ZNV3x4vV54vcZP7olk+htrsX5fNW6dswK7nrxIc51I84CdiUIv1bazgVVj0rCumDSsa8R9x8vyEcx2ad1O7EKrdzxBiJy1Id+UU0or1B5K6JIipH0TdQJfIBCAz+fTfG/NmjUAgK5dI0+QbYXaxuaI61jJ2IgGt8uF28/qDbcLeDDU4dIOZgJOw8qrm2hr61Zlu4gVW/WeRAVBiGz5MLDGSPuJOLKOgVyoMdtFCbOiCGnfWLJ8zJgxA5MnT0aPHj1QW1uLuXPnYtGiRZg/fz62b9+OuXPn4sILL0RhYSHWrVuHadOmYdy4cRgxYkS8xh9z0lM9EdeJfK8LX2GkrImc9j7Dt3G7gdP7FmHjbyeZGpceZtwXdkzX6pgPsXy8nhXlSH0T9lc3Gu5TYfkwESjbkVEIMYecs1mULqkkDoQQYgtL4uPQoUO46aabcODAAeTl5WHEiBGYP38+zjvvPFRUVGDhwoX4y1/+gvr6epSVlWHKlCl45JFH4jX2uGDigd+E20X5+ttfnWe5JHpwP8EdRSM8AHtuF73LkOpxoTlU4dWlynZJDT2q600GTS0B7Td0BqX3tO+UJ11We9WHlg9C2jeWZsQXXnhB972ysjIsXrw46gElG3OVPSO5XZTv52Wk2po8YtXbRG8vCr+5SQecN8WDZn+L5j5Ey0c0E6WZnh1OmWxcOn8T1XeAF4eQdgeLNqswY/mwmu1id66M1cOumeNHKlQmkp6qXE8ukFIjuF3MYCaQ0CHag11tDXCKACWko0LxoSKS9tiwrxo7j9QbruNWTBrmJg6tVWJ1g9VzX8jLxIe5XXQuhDdF6QJSxHyEBEyW136/QjO1LZwy8bhMWIGciot3LkLaNfwJq4iU5XH3K6si7iNW1RdjNcnquZLkHXPNul3Ulg+tjJkuua3pwbOuG2VylOI4Ij/tO2UiVgoxh5y0SZwiQAnpqFB8qIhk+ag5YS0V1+wtUss6EauYDz3kQiss1VZnG3Xwq9LtEvw6leZnSMtG9+yET6aNszU+M7EqHRlmdOjDejCEtG/s28c7CBXHGlCc6211J0RQH/5A5KAQO5YPrdViNcfKjTkDu+SgvG8hJg8rQUOTX1pudpxq8aGVatslNx1XjemO4w3N6JLrNVU7RWscumNyyGyjSIV2iOAyC+ueENK+cbT4+GbnMVz13FIMLc3FT8b3w+Rh+t14V+85jl++vR71sglbD3XMh11i53Zp5a0flyMnPRUA8Ommg9JyteVjYJdsrK2oCtuXN0VpLNNKtQWAP17RWvLdymmYiXMYWmpcM6WjQMGhj7LjL68TIe0NR4uP55cEm599t78GU+euxq9+METT8OEPCLj82a9N79eW5UNjWazcLnL3SmZa60cuN+KoA04fvnAIMtNS8MOTu+HphVuwaPNhABqWD9kY01L0vHjmz8OoyNiH952Jt1btxX3n9je9v/YMO7fqw+tBSPvG0eJDHVv61qq9igwQkVeX77a0X7lJODrLh/1t5cjPSL5P+bmqa3PkZabiN5cMBQBMP2+ATHwoBYbf37qPLK92MTQr56G8dsoNh5bmOcbqocZsQLBTYMwHIe0bR4uPsE6ugqCZYrpg48HwhUb7jVHMR8zcLrJzkk/oZvq3AJAqmgJAmkc5C7YEWquWyq0qciKZxScOLsZd4/qG1m1d7vSnW3a11YfXg5D2jaPFR4pKfMTqhqZMF41iP3GI+ZBjInYWANApM1X6Wz2m3kVZ+MGIrijMStMv4x5h//+8+RTN/Tt9fuHTvT5O/24Q0t5xtPhQT5Zut3ZNDKsBbfYsH3FMtdWxcJg0fKBP52w8ftkwdM724pPvKhXvuVwu/C1CLY9YB5w6BQZV6uOiSCWkXeNoT3KY+HC5TE/IRtipz3B630KN/UQ/FiO04lv0uPG0npg0rMTWI7iVtEhTqbYOgV1tCSEdFWeLD9Xk5tIRH1bv+3YqU549oDNeuX0sXrxV2wURDfpulxgoLRPYtXw4HVo+CCEdFUeLD7Wq2HWkHodrfVHv1m3D8uFyuXBm/yJ0zWutDhq7VNuY7AaAveJO1sQHLR8iyiJjSRsGIYTEHEfHfKgn0mqd0ulW50Bl0KT9eJGYVTjV7e2SKMuH+RPhhNsKhZg5eGkIaX842/IRJ6IJmpRvG01rejl6GuOMfkUAgG6yXizxwMpZRCPcOhoOP33TsNQ6Ie0Pio8I/OZ/30kFtvRIT3Xjj1eMgNsFPHXVSFVjOWs3RrlQiEedDznFOelY8+vz8PnPxsfkOHpYOQ9mu7SisAI5/WIQQjoUjna7mOHFr3dFXKexOYCrxpThhyd3Q6rHjU0HaqT3rM4ZimqksYr5MHgvPzPN2r5suGqsaCjFKTv80Z9dbQkhHRVaPmKI2FI+Vq6D2JX5iF1sh509WTkNTritKIuMOfxiGOBwjUpIu4TiIw4oioxZvMJynRCzImMxxFaQqqXeLq04Pchyxa7j0t9t8KtACCG2cbT4iNfc5ooi5kNOW5x87WgPK+ehjJchIk4PviWEdCwcLT7ihbK8urVt5WmxMUu1jWFGbfzdLq1/t0XxlSx4KfRpixZCQogxFB8x4LKTShWvoykRrnC7xKzCaQxjPmwFnNq0fHBOkaAQC+e2M3pjZFk+zhvSJdlDIYRYxNHZLrG6nffvkqPcbxSFwhKZapuofdm1fNDV0Aof7sP59cVDkj0EQohNaPkwwOxTvlokRJPtkpnmad1PAlJtrdK/S7blbazV+WC2ixbMdiGEdCQcbfmIREvA3LTtUUk4+Vzb1BKwdMxeRVm455x+yM9MtbSdEbFMtb377L5oaglgohVTN7NdosbFxwRCSAfC0eIj0tzW0OQ3tR8jy8eeYw2Wx/WzCwZa3iZRpKd68PNJgyxtY0VDpMqUHLVHKxRihJCOBJ+nDDhhUnyoo+3b2kSRmPZx+li5GhkytxNjPlrhlSCEdCQoPgyob2oxtZ5afLS5OTPJ6sOKiMhIlcW8tLXrmETamqAlhJBocLT4iBTE1+Cz53Zpa/NEsi0fVkREWorM7cLnfYm29p0ihJBocLT4iESDTctHW3tKjWXAqR2siAgPs100aWNfKUIIiQqKDwPMBpyqi4HJxccPRnSN6ZjskGzLh6WutrJvJCfcVtqaoCWEkGhwtPiIdD83G/Ohrschfzm0NM/qsByN3IrEgNNWUmgGIoR0IBwtPiJh2vKhrvMhczOo30sGSfa6WHpqV7pdOOGKUIgRQjoSbWBqbLs0+MzGfCgvo7wglPq9ZBDL3i52sOZ2YVdbQgjp6CR/ZmzDNPtNVjg1iPnwtIEZNNmWDyuXQGH54LeTEEI6JLy9G9AcMFcaXe1akbvn20K776SLDwPTR8/CTMVrxnwQQkjHh+XVDfCbtHwYlVePVXO49ozWJXj80qE4VOvDNaf2UK5LtwshhHR4HC0+IvHnBVtMrWdU4VTtkkkGgWTX+dC4Bp1z0nFjea+w5Qw4JYSQjg/dLjFAbd1QZrskfwL1m+zOm0j0Los8zsPp4mPNr88DANxxZu8kj4QQQmKLwy0fsZncwgNOZe9RfAAIWoPkBhi9eI62YClqK+RnpmHXkxclexiEEBJzaPmIAUbl1duE+Eh2xCm04mK015Nfr2S7iwghhMQHio8YYNRYri24DswGzsYT9VXQuyxuig9CCOnwOFp8xEoXhAectr5uMZmuG0/aguVDfa3NuF3awLAJIYTEAUeLj1hhVELdn3ztIQUsXji8JGljUHe21dN9bcFNRQghJL44POA0Nmi5Vn50dh98te0ILhqe/K62908cgHEDOmNYt+Q1uVNfIj13FGukEEJIx8fR4iNWU1uKRh3wGZMHx2jv0eNxuzCmV0FSxxDudtFeT969lZkvhBDSMbHkdpk9ezZGjBiB3Nxc5Obmory8HB9//LH0fmNjI6ZOnYrCwkJkZ2djypQpOHjwYMwHHStiFVLAHiSRUbtddC0fMvFBwwchhHRMLE2b3bt3x5NPPolVq1Zh5cqVOPfcc3HppZfiu+++AwBMmzYN77//Pt58800sXrwY+/fvx+WXXx6XgceCWAU0Mk4hMmYtH3LodiGEkI6JJbfLxRdfrHj9xBNPYPbs2Vi2bBm6d++OF154AXPnzsW5554LAJgzZw4GDx6MZcuW4bTTTovdqGNGbNQH3QORCUu1NeH0KshKi89gCCGEJBXbMR9+vx9vvvkm6uvrUV5ejlWrVqG5uRkTJ06U1hk0aBB69OiBpUuX6ooPn88Hn88nva6pqbE7JMvEyvLBJ/TImC0yBgB/vnIkDtX6MKBLTpxHRQghJBlYFh/r169HeXk5GhsbkZ2djXfeeQdDhgzBmjVrkJaWhvz8fMX6Xbp0QWVlpe7+Zs6ciccee8zywGNBrIpY0fJhApN1PgBgyujucR4MIYSQZGI5VHLgwIFYs2YNli9fjh//+Me4+eabsXHjRtsDmDFjBqqrq6V/FRUVtvdlFcZ8JA71FeIlI4QQ52LZ8pGWloZ+/foBAEaPHo0VK1bgmWeewdVXX42mpiZUVVUprB8HDx5ESYl+cSuv1wuv12t95DEgGu3hdgFivza6XSKjtnTQWEQIIc4l6iTRQCAAn8+H0aNHIzU1FZ9++qn03ubNm7Fnzx6Ul5dHe5i4EI3lQ17bg26XyKj1mZHbhRBCSMfGkuVjxowZmDx5Mnr06IHa2lrMnTsXixYtwvz585GXl4fbb78d06dPR0FBAXJzc3HvvfeivLy8jWa6AIJF28dJZflYU1EFIFTbw4/Wv4khYZaPJI2DEEJI8rEkPg4dOoSbbroJBw4cQF5eHkaMGIH58+fjvPPOAwA8/fTTcLvdmDJlCnw+Hy644AI8++yzcRl4TLBo+bjspFJJfMhTRWn5iEx4zAevGSGEOBVL4uOFF14wfD89PR2zZs3CrFmzohpUorDqddFzFWiVVydK7BQZI4QQ0jFx9KwpWAz60IsrpfaIjFq40fJBCCHOxdHTpuV4U9mEmeqRuV2Y7RIRXiFCCCEizhYfFtWH2wWU5qUDAMb2KZQt59QaCfUl4jUjhBDnYru8ekfAcswHXFj04Dnwtfjx+4++l5bT8hEZdS8Xag9CCHEuzhYfNmI+0lLcwX8eZrtYQa3PaPkghBDn4my3i8X15fNlqqf10rHCaWRY4ZQQQoiIo8WHVfUhn0BTU5x96aKFeo0QQpyLo2dQqxVO5fOl3PJBIhNu6aD6IIQQp+LoGdR6tkvrhCmP+SCRCc92Sc44CCGEJB+KDwvIJ9AUWj4sEZ7tQvVBCCFOxdEzaMBytkvrhJntdXSiUNTQ8kEIIc7F0eIjmmyXScNK0DnHi5vLe8Z0TE6BqbaEEOJcHP34bt3t0jphFmV7seLhiZZrhTgVq8G9hBBCOi6OtnxYtX1ouQoYu2AP1kYhhBDn4mjxYdnywfTQmMErSQghzsXZ4sPi+nxYt49a6DHmgxBCnIuzxYeO6ePUXgWayzlf2kd9qXktCSHEuThbfOgsv3B4CSYNLQlbzviO2MFLSQghzsXZ4kNDfXTLz8CN5b00J0fOl7GD8TOEEOJcnC0+NJbdNa4PPG6XpvhgnELsYPwMIYQ4F2eLDw3Thzgpaj2ZU3vEDrqwCCHEuThafGghTYq0fMQVWj4IIcS5OFp8aMV8eEKzotbcSO1hH7WViTEfhBDiXJwtPjSiPiS3i4bSoKvAPuor7XL0N48QQpyNo6cALcuHKDC0ZAZdBfZpagkoXvNSEkKIc3GU+Ji3oRIX/9+X2HG4DoC2+BDjOrRTbTll2uVofZPiNa1IhBDiXBwlPu5+ZRXW76vG1LnfAjB2u2hBy0fs4KUkhBDn4ijxIbLrSD0AIGBk+dDakDMmIYQQEjWOFB9N/lD8gWbMh/h/uNJgqi0hhBASPY4UH/6QyUPb7WKQahvPQTkMD31YhBDiWBwpPkSMAk41i4xxwowJj1w0GOmpnmQPgxBCSJJwtvjQWGZUXp1P67HhjrP6JHsIhBBCkoizxYeG6cNlkGrrYcyHbaaM6p7sIRBCCGkjOFt8aCxz63tdaPmIAl46QgghIs4WH0a9XbQsH5xBbUOjESGEEBFHiY9h3XKlv/0BrVwXebYLYz5iCavDEkIIEXGU+PCmtGZYNLUENE0fRk/orPNhH146QgghIo4SHykyy0VzIGBo+fBrCJMUWj5sQ/FBCCFExFHiQ+42aW4JGNb5CGi8SbdLNPDaEUIICeIo8SGn2S8YN5bTEiYUH7ah5YMQQoiIo8SH3JjR7Ne2fBi1eqfbxT68coQQQkQcJT7krpQmf0Cnq63+9gw4tQ8vHSGEEBFHiQ+51ghaPjTcLiH1oRWMypgP+zDVlhBCiIijxIdcUTS3aMkLY8sHxYd9eOkIIYSIOEp8yN0uzQHrMR8UH/Yxuq6EEEKchaPEh8Lt0hLQyXYJuV20Um05gRJCCCFR4yzxIbd8+AXt3i60fMQF6jZCCCEilsTHzJkzccoppyAnJwfFxcW47LLLsHnzZsU648ePh8vlUvy7++67YzpouwTUqbYa6xhNkhQf9mHAKSGEEBFL4mPx4sWYOnUqli1bhgULFqC5uRnnn38+6uvrFevdeeedOHDggPTvj3/8Y0wHbRe52GjSy3Zx6We7UHvYh5YPQgghIilWVp43b57i9Ysvvoji4mKsWrUK48aNk5ZnZmaipKQkNiOMIUq3i05vF7e4buuy287ojU6ZqQyajAIKN0IIISJRxXxUV1cDAAoKChTLX331VRQVFWHYsGGYMWMGGhoadPfh8/lQU1Oj+BcvzFQ41Sok9uuLh+DeCf3jNi4nUFaQmewhEEIIaSNYsnzICQQCeOCBB3DGGWdg2LBh0vLrrrsOPXv2RGlpKdatW4eHHnoImzdvxttvv625n5kzZ+Kxxx6zOwxLyLNbmlsEHbeLuC6JJdee2gN7jjbgrAGdkz0UQgghSca2+Jg6dSo2bNiAL7/8UrH8rrvukv4ePnw4unbtigkTJmD79u3o27dv2H5mzJiB6dOnS69rampQVlZmd1iGBAKtf9c0NusEnNI/EA9SPW488oMhyR4GIYSQNoAt8XHPPffggw8+wJIlS9C9e3fDdceOHQsA2LZtm6b48Hq98Hq9doZhGbnY+N2Hm5DmCfc6sX8LIYQQEl8siQ9BEHDvvffinXfewaJFi9C7d++I26xZswYA0LVrV1sDjCVqN0uTPxC2juR20QoIIYQQQkjUWBIfU6dOxdy5c/Hee+8hJycHlZWVAIC8vDxkZGRg+/btmDt3Li688EIUFhZi3bp1mDZtGsaNG4cRI0bE5QSsYEZP0PJBCCGExBdL4mP27NkAgoXE5MyZMwe33HIL0tLSsHDhQvzlL39BfX09ysrKMGXKFDzyyCMxG3A0aJVTV0PtQQghhMQXy24XI8rKyrB48eKoBhRPrFg+6HQhhBBC4oOjersETKgPllAnhBBC4oujxIcZa0ZLgDYPQgghJJ44SnyYUR/NLQHT6xJCCCHEOo4SH5HcLhcOL0HPwmAZcDPBqYQQQgixju0Kp+0RIzmR7U3Bs9ePTthYCCGEEKdCy0cIdZgpa4wRQggh8cFR4sNQUDDJhRBCCEkIFB+EEEIISSgOEx/m1QeFCiGEEBIfnCU+kj0AQgghhDhMfBioD4Z8EEIIIYnBUeLDTHl1Edb5IIQQQuKDo8SHYbIL29kSQgghCcFZ4oPGDEIIISTpOEx8MNuFEEIISTbOEh8G76m9LtQehBBCSHxwlvigOYMQQghJOo4SHwELqbbUKYQQQkh8cJT4oOWDEEIIST4OEx8G7yVuGIQQQoijcZb4MHjP71e/SzlCCCGExANniQ8D00dzIJDAkRBCCCHOxVHiwyjgtCXM8kEIIYSQeOAo8WHUr6VFpUwYm0oIIYTEB2eJDwNB8aOz+yRuIIQQQoiDSUn2ABKJnvh47sbRmDCoOLGDIYQQQhyKsywfOm6XUT06IcXjVq1LCCGEkHjgLPGhoyjUfV0AIMCgD0IIISQuOEp86AkKDe1hmBlDCCGEEPs4SnyIeuKK0d0Vy90apg+WYieEEELig7PER0hPeFRiQ8vtQu1BCCGExAfHiA+5JcPjUYkPDccLYz4IIYSQ+OAg8dH6t9ryoRX0QfFBCCGExAfHiA+5mPC4I7tdGHBKCCGExAfHiA+5lggTH1rr0/JBCCGExAXniA+52yXM8qGV7RLvERFCCCHOxDHiQ+52MRHywZgPQgghJE44RnzISTER85Ge6knQaAghhBBn4RjxYZTtopVq+8QPh6NfcTb+fOXIeA+NEEIIcRSO6Word6O4TVg+ehdlYeH0s+M9LEIIIcRxOMfyIfvbTIVTQgghhMQH54gPI8uHZsgpIYQQQuKBY8RHwDDVNsGDIYQQQhyMY8SH3O8Slu2S4KEQQgghTsYx4kMRcBoW80H5QQghhCQKx4gPq+XVCSGEEBIfnCM+LKbaEkIIISQ+WBIfM2fOxCmnnIKcnBwUFxfjsssuw+bNmxXrNDY2YurUqSgsLER2djamTJmCgwcPxnTQdggYFRmj+iCEEEIShiXxsXjxYkydOhXLli3DggUL0NzcjPPPPx/19fXSOtOmTcP777+PN998E4sXL8b+/ftx+eWXx3zgVhEgj/loXU7dQQghhCQWSxVO582bp3j94osvori4GKtWrcK4ceNQXV2NF154AXPnzsW5554LAJgzZw4GDx6MZcuW4bTTTovdyK0S0h5ulzLglNqDEEIISSxRxXxUV1cDAAoKCgAAq1atQnNzMyZOnCitM2jQIPTo0QNLly7V3IfP50NNTY3iXzwQ3S4ul0th7aDLhRBCCEkstsVHIBDAAw88gDPOOAPDhg0DAFRWViItLQ35+fmKdbt06YLKykrN/cycORN5eXnSv7KyMrtDMkR0u7igFByUHoQQQkhisS0+pk6dig0bNuD111+PagAzZsxAdXW19K+ioiKq/ekhSG4XF2M+CCGEkCRiq6vtPffcgw8++ABLlixB9+7dpeUlJSVoampCVVWVwvpx8OBBlJSUaO7L6/XC6/XaGYYlpCJjYTEfVB+EEEJIIrFk+RAEAffccw/eeecdfPbZZ+jdu7fi/dGjRyM1NRWffvqptGzz5s3Ys2cPysvLYzNim8i0h9LaQe1BCCGEJBRLlo+pU6di7ty5eO+995CTkyPFceTl5SEjIwN5eXm4/fbbMX36dBQUFCA3Nxf33nsvysvLk5vpIiPodmHMByGEEJIsLImP2bNnAwDGjx+vWD5nzhzccsstAICnn34abrcbU6ZMgc/nwwUXXIBnn302JoONBtHt4lK7Xag+CCGEkIRiSXzIS5TrkZ6ejlmzZmHWrFm2BxUP5AGnilRb2j4IIYSQhOKY3i6S5QPKCqduag9CCCEkoThGfEg2G5eqzgf9LoQQQkhCcYz4cAHI8aYgx5vCgFNCCCEkidiq89Ee6dM5G+sfuwAA8OkmWZddqg9CCCEkoTjG8iFHGXBKCCGEkETiUPHRKjlMJPAQQgghJIY4UnzIYz5qfS1JHAkhhBDiPBwqPpI9AkIIIcS5OFJ8EEIIISR5OFJ8NDYHkj0EQgghxLE4UnzUM86DEEIISRqOFB8ZaZ5kD4EQQghxLI4UHxMGFSd7CIQQQohjcaT4SPG4keN1THFXQgghpE3hSPFBCCGEkOThXPHBWh+EEEJIUnCu+CCEEEJIUnCs+KDhgxBCCEkOjhUfhBBCCEkOjhUf8s62hBBCCEkcDhYfyR4BIYQQ4kwcKz4IIYQQkhwcKz5o+CCEEEKSg2PFByGEEEKSA8UHIYQQQhIKxQchhBBCEopjxQdTbQkhhJDk4FjxQQghhJDk4FjxQbsHIYQQkhycKz6oPgghhJCk4FjxQQghhJDk4GDxQdMHIYQQkgwcLD4IIYQQkgwcKz4Y80EIIYQkB8eKjxQ31QchhBCSDBwrPi4eWQoAKMlNT/JICCGEEGeRkuwBJIufnj8A3Ttl4JyBxckeCiGEEOIoHCs+vCke3FTeK9nDIIQQQhyHY90uhBBCCEkOFB+EEEIISSgUH4QQQghJKBQfhBBCCEkoFB+EEEIISSgUH4QQQghJKBQfhBBCCEkoFB+EEEIISSgUH4QQQghJKJbFx5IlS3DxxRejtLQULpcL7777ruL9W265BS6XS/Fv0qRJsRovIYQQQto5lsVHfX09Ro4ciVmzZumuM2nSJBw4cED699prr0U1SEIIIYR0HCz3dpk8eTImT55suI7X60VJSYntQRFCCCGk4xKXmI9FixahuLgYAwcOxI9//GMcPXpUd12fz4eamhrFP0IIIYR0XGLe1XbSpEm4/PLL0bt3b2zfvh2//OUvMXnyZCxduhQejyds/ZkzZ+Kxxx4LW04RQgghhLQfxHlbEISI67oEM2vpbexy4Z133sFll12mu86OHTvQt29fLFy4EBMmTAh73+fzwefzSa/37duHIUOG2B0SIYQQQpJIRUUFunfvbrhOzC0favr06YOioiJs27ZNU3x4vV54vV7pdXZ2NioqKpCTkwOXyxXv4cWdmpoalJWVoaKiArm5uckejiPhZ5B8+BkkH34GyaejfwaCIKC2thalpaUR1427+Ni7dy+OHj2Krl27mlrf7XZHVEztkdzc3A75ZWtP8DNIPvwMkg8/g+TTkT+DvLw8U+tZFh91dXXYtm2b9Hrnzp1Ys2YNCgoKUFBQgMceewxTpkxBSUkJtm/fjp///Ofo168fLrjgAquHIoQQQkgHxLL4WLlyJc455xzp9fTp0wEAN998M2bPno1169bhpZdeQlVVFUpLS3H++efj8ccfV7hWCCGEEOJcLIuP8ePHG0ayzp8/P6oBdTS8Xi8effRRiq8kws8g+fAzSD78DJIPP4NWosp2IYQQQgixChvLEUIIISShUHwQQgghJKFQfBBCCCEkoVB8EEIIISShUHyY4De/+Q1cLpfi36BBg6T3GxsbMXXqVBQWFiI7OxtTpkzBwYMHFfvYs2cPLrroImRmZqK4uBgPPvggWlpaFOssWrQIo0aNgtfrRb9+/fDiiy8m4vTaBfv27cMNN9yAwsJCZGRkYPjw4Vi5cqX0viAI+PWvf42uXbsiIyMDEydOxNatWxX7OHbsGK6//nrk5uYiPz8ft99+O+rq6hTrrFu3DmeddRbS09NRVlaGP/7xjwk5v/ZAr169wn4HLpcLU6dOBcDfQbzx+/341a9+hd69eyMjIwN9+/bF448/rsg+5O8g/tTW1uKBBx5Az549kZGRgdNPPx0rVqyQ3udnYBKBROTRRx8Vhg4dKhw4cED6d/jwYen9u+++WygrKxM+/fRTYeXKlcJpp50mnH766dL7LS0twrBhw4SJEycK3377rfDRRx8JRUVFwowZM6R1duzYIWRmZgrTp08XNm7cKPzf//2f4PF4hHnz5iX0XNsix44dE3r27CnccsstwvLly4UdO3YI8+fPF7Zt2yat8+STTwp5eXnCu+++K6xdu1a45JJLhN69ewsnTpyQ1pk0aZIwcuRIYdmyZcIXX3wh9OvXT7j22mul96urq4UuXboI119/vbBhwwbhtddeEzIyMoTnnnsuoefbVjl06JDiN7BgwQIBgPD5558LgsDfQbx54oknhMLCQuGDDz4Qdu7cKbz55ptCdna28Mwzz0jr8HcQf6666iphyJAhwuLFi4WtW7cKjz76qJCbmyvs3btXEAR+Bmah+DDBo48+KowcOVLzvaqqKiE1NVV48803pWWbNm0SAAhLly4VBEEQPvroI8HtdguVlZXSOrNnzxZyc3MFn88nCIIg/PznPxeGDh2q2PfVV18tXHDBBTE+m/bHQw89JJx55pm67wcCAaGkpET405/+JC2rqqoSvF6v8NprrwmCIAgbN24UAAgrVqyQ1vn4448Fl8sl7Nu3TxAEQXj22WeFTp06SZ+JeOyBAwfG+pQ6BPfff7/Qt29fIRAI8HeQAC666CLhtttuUyy7/PLLheuvv14QBP4OEkFDQ4Pg8XiEDz74QLF81KhRwsMPP8zPwAJ0u5hk69atKC0tRZ8+fXD99ddjz549AIBVq1ahubkZEydOlNYdNGgQevTogaVLlwIAli5diuHDh6NLly7SOhdccAFqamrw3XffSevI9yGuI+7Dyfzvf//DmDFjcOWVV6K4uBgnn3wy/vGPf0jv79y5E5WVlYrrl5eXh7Fjxyo+g/z8fIwZM0ZaZ+LEiXC73Vi+fLm0zrhx45CWliatc8EFF2Dz5s04fvx4vE+zXdHU1IRXXnkFt912G1wuF38HCeD000/Hp59+ii1btgAA1q5diy+//BKTJ08GwN9BImhpaYHf70d6erpieUZGBr788kt+Bhag+DDB2LFj8eKLL2LevHmYPXs2du7cibPOOgu1tbWorKxEWloa8vPzFdt06dIFlZWVAIDKykrFDVd8X3zPaJ2amhqcOHEiTmfWPtixYwdmz56N/v37Y/78+fjxj3+M++67Dy+99BKA1muodf3k17e4uFjxfkpKCgoKCix9TiTIu+++i6qqKtxyyy0AwN9BAvjFL36Ba665BoMGDUJqaipOPvlkPPDAA7j++usB8HeQCHJyclBeXo7HH38c+/fvh9/vxyuvvIKlS5fiwIED/AwsEPeuth0B8ckCAEaMGIGxY8eiZ8+eeOONN5CRkZHEkTmDQCCAMWPG4Pe//z0A4OSTT8aGDRvw97//HTfffHOSR+dMXnjhBUyePNlU62wSG9544w28+uqrmDt3LoYOHYo1a9bggQceQGlpKX8HCeTll1/Gbbfdhm7dusHj8WDUqFG49tprsWrVqmQPrV1By4cN8vPzMWDAAGzbtg0lJSVoampCVVWVYp2DBw+ipKQEAFBSUhIW9S++jrRObm6u4wVO165dMWTIEMWywYMHS64v8RpqXT/59T106JDi/ZaWFhw7dszS50SA3bt3Y+HChbjjjjukZfwdxJ8HH3xQsn4MHz4cN954I6ZNm4aZM2cC4O8gUfTt2xeLFy9GXV0dKioq8M0336C5uRl9+vThZ2ABig8b1NXVYfv27ejatStGjx6N1NRUfPrpp9L7mzdvxp49e1BeXg4AKC8vx/r16xVfuAULFiA3N1eaVMvLyxX7ENcR9+FkzjjjDGzevFmxbMuWLejZsycAoHfv3igpKVFcv5qaGixfvlzxGVRVVSmeTj777DMEAgGMHTtWWmfJkiVobm6W1lmwYAEGDhyITp06xe382htz5sxBcXExLrroImkZfwfxp6GhAW638pbt8XgQCAQA8HeQaLKystC1a1ccP34c8+fPx6WXXsrPwArJjnhtD/z0pz8VFi1aJOzcuVP46quvhIkTJwpFRUXCoUOHBEEIphj26NFD+Oyzz4SVK1cK5eXlQnl5ubS9mGJ4/vnnC2vWrBHmzZsndO7cWTPF8MEHHxQ2bdokzJo1iymGIb755hshJSVFeOKJJ4StW7cKr776qpCZmSm88sor0jpPPvmkkJ+fL7z33nvCunXrhEsvvVQzve3kk08Wli9fLnz55ZdC//79FeltVVVVQpcuXYQbb7xR2LBhg/D6668LmZmZHSq9LVr8fr/Qo0cP4aGHHgp7j7+D+HLzzTcL3bp1k1Jt3377baGoqEj4+c9/Lq3D30H8mTdvnvDxxx8LO3bsED755BNh5MiRwtixY4WmpiZBEPgZmIXiwwRXX3210LVrVyEtLU3o1q2bcPXVVytqTJw4cUL4yU9+InTq1EnIzMwUfvjDHwoHDhxQ7GPXrl3C5MmThYyMDKGoqEj46U9/KjQ3NyvW+fzzz4WTTjpJSEtLE/r06SPMmTMnEafXLnj//feFYcOGCV6vVxg0aJDw/PPPK94PBALCr371K6FLly6C1+sVJkyYIGzevFmxztGjR4Vrr71WyM7OFnJzc4Vbb71VqK2tVayzdu1a4cwzzxS8Xq/QrVs34cknn4z7ubUn5s+fLwAIu7aCwN9BvKmpqRHuv/9+oUePHkJ6errQp08f4eGHH1akY/J3EH/+85//CH369BHS0tKEkpISYerUqUJVVZX0Pj8Dc7gEQVYejxBCCCEkzjDmgxBCCCEJheKDEEIIIQmF4oMQQgghCYXigxBCCCEJheKDEEIIIQmF4oMQQgghCYXigxBCCCEJheKDEEIIIQmF4oMQQgghCYXigxBCCCEJheKDEEIIIQmF4oMQQgghCeX/A5qrqAXg7MdNAAAAAElFTkSuQmCC", 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" ] @@ -504,11 +505,11 @@ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", "\n", - "spectra = rubixdata.stars.datacube # Spectra of all stars\n", + "#spectra = rubixdata.stars.datacube # Spectra of all stars\n", "spectra_sharded = shard_rubixdata # Spectra of all stars\n", - "print(spectra.shape)\n", + "#print(spectra.shape)\n", "\n", - "plt.plot(wave, spectra[12,12,:])\n", + "#plt.plot(wave, spectra[12,12,:])\n", "plt.plot(wave, spectra_sharded[12,12,:])" ] }, @@ -521,18 +522,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'rubixdata' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#NBVAL_SKIP\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# get the spectra of the visible wavelengths from the ifu cube\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m visible_spectra \u001b[38;5;241m=\u001b[39m \u001b[43mrubixdata\u001b[49m\u001b[38;5;241m.\u001b[39mstars\u001b[38;5;241m.\u001b[39mdatacube[ :, :, visible_indices[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m 4\u001b[0m sharded_visible_spectra \u001b[38;5;241m=\u001b[39m shard_rubixdata[ :, :, visible_indices[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m#visible_spectra.shape\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name 'rubixdata' is not defined" + ] } ], "source": [ @@ -588,7 +590,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/rubix/core/data.py b/rubix/core/data.py index ae560007..4c27cc90 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -81,18 +81,18 @@ class Galaxy: center: Optional[jnp.ndarray] = None halfmassrad_stars: Optional[jnp.ndarray] = None - def __repr__(self): - representationString = ["Galaxy:"] - for k, v in self.__dict__.items(): - if not k.endswith("_unit"): - if v is not None: - attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - attrString += f", unit = {getattr(self, k + '_unit')}" - representationString.append(attrString) - else: - representationString.append(f"{k}: None") - return "\n\t".join(representationString) + #def __repr__(self): + # representationString = ["Galaxy:"] + # for k, v in self.__dict__.items(): + # if not k.endswith("_unit"): + # if v is not None: + # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + # attrString += f", unit = {getattr(self, k + '_unit')}" + # representationString.append(attrString) + # else: + # representationString.append(f"{k}: None") + # return "\n\t".join(representationString) def tree_flatten(self): """ @@ -154,18 +154,18 @@ class StarsData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - def __repr__(self): - representationString = ["StarsData:"] - for k, v in self.__dict__.items(): - if not k.endswith("_unit"): - if v is not None: - attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - attrString += f", unit = {getattr(self, k + '_unit')}" - representationString.append(attrString) - else: - representationString.append(f"{k}: None") - return "\n\t".join(representationString) + #def __repr__(self): + # representationString = ["StarsData:"] + # for k, v in self.__dict__.items(): + # if not k.endswith("_unit"): + # if v is not None: + # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + # attrString += f", unit = {getattr(self, k + '_unit')}" + # representationString.append(attrString) + # else: + # representationString.append(f"{k}: None") + # return "\n\t".join(representationString) def tree_flatten(self): """ @@ -244,18 +244,18 @@ class GasData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - def __repr__(self): - representationString = ["GasData:"] - for k, v in self.__dict__.items(): - if not k.endswith("_unit"): - if v is not None: - attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - attrString += f", unit = {getattr(self, k + '_unit')}" - representationString.append(attrString) - else: - representationString.append(f"{k}: None") - return "\n\t".join(representationString) + #def __repr__(self): + # representationString = ["GasData:"] + # for k, v in self.__dict__.items(): + # if not k.endswith("_unit"): + # if v is not None: + # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + # attrString += f", unit = {getattr(self, k + '_unit')}" + # representationString.append(attrString) + # else: + # representationString.append(f"{k}: None") + # return "\n\t".join(representationString) def tree_flatten(self): """ @@ -317,11 +317,11 @@ class RubixData: stars: Optional[StarsData] = None gas: Optional[GasData] = None - def __repr__(self): - representationString = ["RubixData:"] - for k, v in self.__dict__.items(): - representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) - return "\n\t".join(representationString) + #def __repr__(self): + # representationString = ["RubixData:"] + # for k, v in self.__dict__.items(): + # representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) + # return "\n\t".join(representationString) # def __post_init__(self): # if self.stars is not None: diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 55f6f363..ba1c715b 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -217,6 +217,7 @@ def run_sharded(self, inputdata): replicate_0d = NamedSharding(mesh, P()) # for scalars replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) + shard_1d = NamedSharding(mesh, P("data")) # for (N,) replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube # — 1) allocate empty instances — @@ -234,28 +235,28 @@ def run_sharded(self, inputdata): # stars stars_spec.coords = shard_2d stars_spec.velocity = shard_2d - stars_spec.mass = replicate_1d - stars_spec.age = replicate_1d - stars_spec.metallicity = replicate_1d - stars_spec.pixel_assignment = replicate_1d + stars_spec.mass = shard_1d + stars_spec.age = shard_1d + stars_spec.metallicity = shard_1d + stars_spec.pixel_assignment = shard_1d stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) - stars_spec.mask = replicate_1d + stars_spec.mask = shard_1d stars_spec.spectra = shard_2d stars_spec.datacube = replicate_3d # gas (same idea) gas_spec.coords = shard_2d gas_spec.velocity = shard_2d - gas_spec.mass = replicate_1d - gas_spec.density = replicate_1d - gas_spec.internal_energy = replicate_1d - gas_spec.metallicity = replicate_1d - gas_spec.metals = replicate_1d - gas_spec.sfr = replicate_1d - gas_spec.electron_abundance = replicate_1d - gas_spec.pixel_assignment = replicate_1d + gas_spec.mass = shard_1d + gas_spec.density = shard_1d + gas_spec.internal_energy = shard_1d + gas_spec.metallicity = shard_1d + gas_spec.metals = shard_1d + gas_spec.sfr = shard_1d + gas_spec.electron_abundance = shard_1d + gas_spec.pixel_assignment = shard_1d gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) - gas_spec.mask = replicate_1d + gas_spec.mask = shard_1d gas_spec.spectra = shard_2d gas_spec.datacube = replicate_3d @@ -265,7 +266,7 @@ def run_sharded(self, inputdata): rubix_spec.gas = gas_spec n = inputdata.stars.coords.shape[0] - pad = (3 - (n % 3)) % 3 + pad = (num_devices - (n % num_devices)) % num_devices if pad: # pad along the first axis @@ -276,7 +277,28 @@ def run_sharded(self, inputdata): inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0,pad))) - + def _shard_pipeline(sharded_rubixdata): + out_local = self.func(sharded_rubixdata) + local_cube = out_local.stars.datacube # shape (25,25,5994) + # in‐XLA all‐reduce across the "data" axis: + #full_cube = lax.psum(local_cube, axis_name="data") + return local_cube # replicated on each device + + shard_pipeline = pjit( + _shard_pipeline, # the function to compile + in_shardings = (rubix_spec,), + out_shardings = replicate_3d, + ) + + with mesh: + partial_cubes = shard_pipeline(inputdata) + partial_cubes = jax.block_until_ready(partial_cubes) + + #final_cube = jnp.sum(partial_cubes, axis=0) + + return partial_cubes + + """ @partial(jax.jit, #how inputs ARE sharded when the function is called in_shardings = (rubix_spec,), @@ -302,6 +324,7 @@ def shard_pipeline(sharded_rubixdata): ) return partial_cubes + """ """ def _shard_pipeline(sharded_rubixdata): From 76e9abf0eb6d04b67b42420a160562d2c37e0ac9 Mon Sep 17 00:00:00 2001 From: anschaible Date: Thu, 24 Apr 2025 17:17:12 +0200 Subject: [PATCH 10/76] comment code --- ...x_pipeline_single_function_shard_map.ipynb | 245 +++++++++--------- rubix/core/pipeline.py | 4 + 2 files changed, 128 insertions(+), 121 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index cfd45a32..c7ad52dc 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -119,23 +119,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-24 15:21:55,657 - rubix - INFO - \n", + "2025-04-24 17:13:28,759 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-04-24 15:21:55,658 - rubix - INFO - Rubix version: 0.0.post415+gd0b5d77\n", - "2025-04-24 15:21:55,659 - rubix - INFO - JAX version: 0.6.0\n", - "2025-04-24 15:21:55,659 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" + "2025-04-24 17:13:28,761 - rubix - INFO - Rubix version: 0.0.post415+gd0b5d77\n", + "2025-04-24 17:13:28,762 - rubix - INFO - JAX version: 0.6.0\n", + "2025-04-24 17:13:28,763 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" ] } ], @@ -169,7 +169,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 1000,\n", + " \"subset_size\": 5000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -314,127 +314,129 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-24 17:13:29,658 - rubix - INFO - Getting rubix data...\n", + "2025-04-24 17:13:29,660 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-24 17:13:29,724 - rubix - INFO - Centering stars particles\n", + "2025-04-24 17:13:31,832 - rubix - WARNING - The Subset value is set in config. Using only subset of size 5000 for stars\n", + "2025-04-24 17:13:31,833 - rubix - INFO - Data loaded with 5000 star particles and 0 gas particles.\n", + "2025-04-24 17:13:31,834 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-24 17:13:31,834 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-24 17:13:31,835 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-24 17:13:31,836 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-24 17:13:31,857 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-24 17:13:32,283 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-24 17:13:32,299 - rubix - INFO - Getting cosmology...\n", + "2025-04-24 17:13:32,390 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-24 17:13:32,489 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-24 17:13:32,639 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-24 17:13:32,666 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-24 17:13:33,323 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-24 17:13:33,324 - rubix - INFO - Compiling the expressions...\n", + "2025-04-24 17:13:33,325 - rubix - INFO - Running the pipeline on the input data...\n", + "2025-04-24 17:13:33,326 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-24 17:13:33,420 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-24 17:13:33,427 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-24 17:13:33,446 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-24 17:13:33,446 - rubix - DEBUG - Input shapes: Metallicity: 5000, Age: 5000\n", + "2025-04-24 17:13:33,564 - rubix - DEBUG - Calculation Finished! Spectra shape: (5000, 5994)\n", + "2025-04-24 17:13:33,565 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-24 17:13:33,570 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-24 17:13:33,570 - rubix - DEBUG - Doppler Shifted SSP Wave: (5000, 5994)\n", + "2025-04-24 17:13:33,571 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-24 17:13:33,639 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-24 17:13:33,642 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-24 17:13:33,643 - rubix - INFO - Convolving with PSF...\n", + "2025-04-24 17:13:33,647 - rubix - INFO - Convolving with LSF...\n", + "2025-04-24 17:13:33,653 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", + "2025-04-24 17:13:46,628 - rubix - INFO - Pipeline run completed in 14.79 seconds.\n" ] } ], "source": [ "#NBVAL_SKIP\n", "\n", - "#inputdata = pipe.prepare_data()\n", - "#rubixdata = pipe.run(inputdata)" + "inputdata = pipe.prepare_data()\n", + "rubixdata = pipe.run(inputdata)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-24 15:21:56,525 - rubix - INFO - Getting rubix data...\n", - "2025-04-24 15:21:56,527 - rubix - INFO - Loading data from IllustrisAPI\n", - "2025-04-24 15:21:56,528 - rubix - INFO - Reusing existing file galaxy-id-14.hdf5. If you want to download the data again, set reuse=False.\n", - "2025-04-24 15:21:56,559 - rubix - INFO - Loading data into input handler\n", - "2025-04-24 15:21:56,561 - rubix - DEBUG - Loading data from Illustris file..\n", - "2025-04-24 15:21:56,562 - rubix - DEBUG - Checking if the fields are present in the file...\n", - "2025-04-24 15:21:56,562 - rubix - DEBUG - Keys in the file: \n", - "2025-04-24 15:21:56,563 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", - "2025-04-24 15:21:56,564 - rubix - DEBUG - Matching fields: {'SubhaloData', 'Header', 'PartType4'}\n", - "2025-04-24 15:21:56,568 - rubix - DEBUG - Found 484076 valid particles out of 484076\n", - "2025-04-24 15:21:56,913 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", - "2025-04-24 15:22:01,771 - rubix - DEBUG - Converting to Rubix format..\n", - "2025-04-24 15:22:01,775 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", - "2025-04-24 15:22:01,776 - rubix - DEBUG - Keys in the particle data: dict_keys(['stars'])\n", - "2025-04-24 15:22:01,776 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", - "2025-04-24 15:22:01,776 - rubix - DEBUG - Matching fields: {'stars'}\n", - "2025-04-24 15:22:01,777 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", - "2025-04-24 15:22:01,777 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", - "2025-04-24 15:22:01,778 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-04-24 15:22:01,778 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", - "2025-04-24 15:22:01,786 - rubix - DEBUG - Converting redshift for galaxy data into \n", - "2025-04-24 15:22:01,788 - rubix - DEBUG - Converting center for galaxy data into kpc\n", - "2025-04-24 15:22:01,789 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", - "2025-04-24 15:22:01,790 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", - "2025-04-24 15:22:01,815 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", - "2025-04-24 15:22:01,822 - rubix - DEBUG - Converting metallicity for particle type stars into \n", - "2025-04-24 15:22:01,876 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", - "2025-04-24 15:22:01,885 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", - "2025-04-24 15:22:01,922 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-04-24 15:22:02,014 - rubix - INFO - Centering stars particles\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Converted to Rubix format!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-24 15:22:04,139 - rubix - WARNING - The Subset value is set in config. Using only subset of size 10000 for stars\n", - "2025-04-24 15:22:04,141 - rubix - INFO - Data loaded with 10000 star particles and 0 gas particles.\n", - "2025-04-24 15:22:04,142 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-24 15:22:04,142 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-24 15:22:04,143 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-24 15:22:04,145 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-24 17:13:46,643 - rubix - INFO - Getting rubix data...\n", + "2025-04-24 17:13:46,647 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-24 17:13:46,677 - rubix - INFO - Centering stars particles\n", + "2025-04-24 17:13:46,712 - rubix - WARNING - The Subset value is set in config. Using only subset of size 5000 for stars\n", + "2025-04-24 17:13:46,713 - rubix - INFO - Data loaded with 5000 star particles and 0 gas particles.\n", + "2025-04-24 17:13:46,714 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-24 17:13:46,714 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-24 17:13:46,715 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-24 17:13:46,719 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 15:22:04,163 - rubix - INFO - Getting cosmology...\n", + "2025-04-24 17:13:46,740 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 15:22:04,607 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-24 17:13:46,763 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 15:22:04,626 - rubix - INFO - Getting cosmology...\n", - "2025-04-24 15:22:04,709 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-24 15:22:04,798 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-24 17:13:46,781 - rubix - INFO - Getting cosmology...\n", + "2025-04-24 17:13:46,839 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-24 17:13:46,898 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 15:22:04,938 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-24 17:13:46,975 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 15:22:04,956 - rubix - INFO - Getting cosmology...\n", + "2025-04-24 17:13:46,998 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 15:22:05,573 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-24 15:22:05,575 - rubix - INFO - Compiling the expressions...\n", - "2025-04-24 15:22:05,575 - rubix - INFO - Number of devices: 4\n", - "2025-04-24 15:22:05,578 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-24 15:22:05,687 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-24 15:22:05,693 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-24 15:22:05,722 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-24 15:22:05,723 - rubix - DEBUG - Input shapes: Metallicity: 10000, Age: 10000\n", - "2025-04-24 15:22:05,869 - rubix - DEBUG - Calculation Finished! Spectra shape: (10000, 5994)\n", - "2025-04-24 15:22:05,870 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-24 15:22:05,876 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-24 15:22:05,877 - rubix - DEBUG - Doppler Shifted SSP Wave: (10000, 5994)\n", - "2025-04-24 15:22:05,878 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-24 15:22:05,949 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-24 15:22:05,952 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-24 15:22:05,953 - rubix - INFO - Convolving with PSF...\n", - "2025-04-24 15:22:05,957 - rubix - INFO - Convolving with LSF...\n", - "2025-04-24 15:22:05,963 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", - "E0424 15:22:24.952429 3558774 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: INTERNAL: jaxlib/gpu/solver_handle_pool.cc:37: operation gpusolverDnCreate(&handle) failed: cuSolver internal error\n", - "E0424 15:22:24.957338 3558777 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: INTERNAL: jaxlib/gpu/solver_handle_pool.cc:37: operation gpusolverDnCreate(&handle) failed: cuSolver internal error\n" + "2025-04-24 17:13:47,037 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-24 17:13:47,038 - rubix - INFO - Compiling the expressions...\n", + "2025-04-24 17:13:47,070 - rubix - INFO - Number of devices: 4\n", + "2025-04-24 17:13:47,075 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-24 17:13:47,201 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-24 17:13:47,209 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-24 17:13:47,247 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-24 17:13:47,248 - rubix - DEBUG - Input shapes: Metallicity: 5000, Age: 5000\n", + "2025-04-24 17:13:47,706 - rubix - DEBUG - Calculation Finished! Spectra shape: (5000, 5994)\n", + "2025-04-24 17:13:47,707 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-24 17:13:47,714 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-24 17:13:47,715 - rubix - DEBUG - Doppler Shifted SSP Wave: (5000, 5994)\n", + "2025-04-24 17:13:47,716 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-24 17:13:47,793 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-24 17:13:47,796 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-24 17:13:47,797 - rubix - INFO - Convolving with PSF...\n", + "2025-04-24 17:13:47,801 - rubix - INFO - Convolving with LSF...\n", + "2025-04-24 17:13:47,808 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n" ] } ], @@ -456,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -477,24 +479,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -505,12 +497,24 @@ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", "\n", - "#spectra = rubixdata.stars.datacube # Spectra of all stars\n", + "spectra = rubixdata.stars.datacube # Spectra of all stars\n", "spectra_sharded = shard_rubixdata # Spectra of all stars\n", "#print(spectra.shape)\n", "\n", - "#plt.plot(wave, spectra[12,12,:])\n", - "plt.plot(wave, spectra_sharded[12,12,:])" + "plt.figure(figsize=(10, 5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"Rubix\")\n", + "plt.xlabel(\"Wavelength [Angstrom]\")\n", + "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "plt.plot(wave, spectra[12,12,:])\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"Rubix Sharded\")\n", + "plt.xlabel(\"Wavelength [Angstrom]\")\n", + "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "plt.plot(wave, spectra_sharded[12,12,:])\n", + "\n", + "plt.show()" ] }, { @@ -522,19 +526,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'rubixdata' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#NBVAL_SKIP\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# get the spectra of the visible wavelengths from the ifu cube\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m visible_spectra \u001b[38;5;241m=\u001b[39m \u001b[43mrubixdata\u001b[49m\u001b[38;5;241m.\u001b[39mstars\u001b[38;5;241m.\u001b[39mdatacube[ :, :, visible_indices[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m 4\u001b[0m sharded_visible_spectra \u001b[38;5;241m=\u001b[39m shard_rubixdata[ :, :, visible_indices[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m#visible_spectra.shape\u001b[39;00m\n", - "\u001b[0;31mNameError\u001b[0m: name 'rubixdata' is not defined" - ] + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index ba1c715b..8d0b18a8 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -265,6 +265,9 @@ def run_sharded(self, inputdata): rubix_spec.stars = stars_spec rubix_spec.gas = gas_spec + #if the particle number is not modulo the device number, we have to padd a few empty particles + # to make it work + # this is a bit of a hack, but it works n = inputdata.stars.coords.shape[0] pad = (num_devices - (n % num_devices)) % num_devices @@ -277,6 +280,7 @@ def run_sharded(self, inputdata): inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0,pad))) + # create the sharded data def _shard_pipeline(sharded_rubixdata): out_local = self.func(sharded_rubixdata) local_cube = out_local.stars.datacube # shape (25,25,5994) From 739f85b124524dfae1d0f68cf6a31eb213145ce2 Mon Sep 17 00:00:00 2001 From: anschaible Date: Fri, 25 Apr 2025 14:33:16 +0200 Subject: [PATCH 11/76] sharding nochmal chunken --- ...x_pipeline_single_function_shard_map.ipynb | 174 +++++++-------- rubix/core/pipeline.py | 203 ++++++++++++++---- 2 files changed, 252 insertions(+), 125 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index c7ad52dc..771c8491 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -119,23 +119,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-24 17:13:28,759 - rubix - INFO - \n", + "2025-04-25 14:18:34,943 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-04-24 17:13:28,761 - rubix - INFO - Rubix version: 0.0.post415+gd0b5d77\n", - "2025-04-24 17:13:28,762 - rubix - INFO - JAX version: 0.6.0\n", - "2025-04-24 17:13:28,763 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" + "2025-04-25 14:18:34,945 - rubix - INFO - Rubix version: 0.0.post417+g76e9abf.d20250424\n", + "2025-04-25 14:18:34,945 - rubix - INFO - JAX version: 0.6.0\n", + "2025-04-25 14:18:34,946 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" ] } ], @@ -169,7 +169,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 5000,\n", + " \"subset_size\": 200000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -185,7 +185,7 @@ " {\"name\": \"MUSE\",\n", " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 1,\"noise_distribution\": \"normal\"},},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", " \"cosmology\":\n", " {\"name\": \"PLANCK15\"},\n", " \n", @@ -321,55 +321,55 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-04-24 17:13:29,658 - rubix - INFO - Getting rubix data...\n", - "2025-04-24 17:13:29,660 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-24 17:13:29,724 - rubix - INFO - Centering stars particles\n", - "2025-04-24 17:13:31,832 - rubix - WARNING - The Subset value is set in config. Using only subset of size 5000 for stars\n", - "2025-04-24 17:13:31,833 - rubix - INFO - Data loaded with 5000 star particles and 0 gas particles.\n", - "2025-04-24 17:13:31,834 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-24 17:13:31,834 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-24 17:13:31,835 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-24 17:13:31,836 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-25 14:18:35,850 - rubix - INFO - Getting rubix data...\n", + "2025-04-25 14:18:35,851 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-25 14:18:35,918 - rubix - INFO - Centering stars particles\n", + "2025-04-25 14:18:38,066 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-04-25 14:18:38,067 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-04-25 14:18:38,067 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-25 14:18:38,068 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-25 14:18:38,069 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-25 14:18:38,071 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:31,857 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:18:38,088 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:32,283 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-25 14:18:38,534 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:32,299 - rubix - INFO - Getting cosmology...\n", - "2025-04-24 17:13:32,390 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-24 17:13:32,489 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-25 14:18:38,548 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:18:38,636 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-25 14:18:38,733 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:32,639 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-25 14:18:38,881 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:32,666 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:18:38,898 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:33,323 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-24 17:13:33,324 - rubix - INFO - Compiling the expressions...\n", - "2025-04-24 17:13:33,325 - rubix - INFO - Running the pipeline on the input data...\n", - "2025-04-24 17:13:33,326 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-24 17:13:33,420 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-24 17:13:33,427 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-24 17:13:33,446 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-24 17:13:33,446 - rubix - DEBUG - Input shapes: Metallicity: 5000, Age: 5000\n", - "2025-04-24 17:13:33,564 - rubix - DEBUG - Calculation Finished! Spectra shape: (5000, 5994)\n", - "2025-04-24 17:13:33,565 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-24 17:13:33,570 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-24 17:13:33,570 - rubix - DEBUG - Doppler Shifted SSP Wave: (5000, 5994)\n", - "2025-04-24 17:13:33,571 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-24 17:13:33,639 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-24 17:13:33,642 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-24 17:13:33,643 - rubix - INFO - Convolving with PSF...\n", - "2025-04-24 17:13:33,647 - rubix - INFO - Convolving with LSF...\n", - "2025-04-24 17:13:33,653 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n", - "2025-04-24 17:13:46,628 - rubix - INFO - Pipeline run completed in 14.79 seconds.\n" + "2025-04-25 14:18:39,487 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-25 14:18:39,488 - rubix - INFO - Compiling the expressions...\n", + "2025-04-25 14:18:39,489 - rubix - INFO - Number of devices: 4\n", + "2025-04-25 14:18:39,491 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-25 14:18:39,596 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-25 14:18:39,602 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-25 14:18:39,629 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-25 14:18:39,630 - rubix - DEBUG - Input shapes: Metallicity: 2000, Age: 2000\n", + "2025-04-25 14:18:39,766 - rubix - DEBUG - Calculation Finished! Spectra shape: (2000, 5994)\n", + "2025-04-25 14:18:39,767 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-25 14:18:39,772 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-25 14:18:39,773 - rubix - DEBUG - Doppler Shifted SSP Wave: (2000, 5994)\n", + "2025-04-25 14:18:39,774 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-25 14:18:39,840 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-25 14:18:39,842 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-25 14:18:39,843 - rubix - INFO - Convolving with PSF...\n", + "2025-04-25 14:18:39,849 - rubix - INFO - Convolving with LSF...\n", + "2025-04-25 14:18:39,855 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-04-25 14:18:57,533 - rubix - INFO - Pipeline run completed in 19.47 seconds.\n" ] } ], @@ -377,7 +377,7 @@ "#NBVAL_SKIP\n", "\n", "inputdata = pipe.prepare_data()\n", - "rubixdata = pipe.run(inputdata)" + "rubixdata = pipe.run_sharded(inputdata)" ] }, { @@ -389,54 +389,57 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-04-24 17:13:46,643 - rubix - INFO - Getting rubix data...\n", - "2025-04-24 17:13:46,647 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-24 17:13:46,677 - rubix - INFO - Centering stars particles\n", - "2025-04-24 17:13:46,712 - rubix - WARNING - The Subset value is set in config. Using only subset of size 5000 for stars\n", - "2025-04-24 17:13:46,713 - rubix - INFO - Data loaded with 5000 star particles and 0 gas particles.\n", - "2025-04-24 17:13:46,714 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-24 17:13:46,714 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-24 17:13:46,715 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-24 17:13:46,719 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-25 14:18:57,576 - rubix - INFO - Getting rubix data...\n", + "2025-04-25 14:18:57,578 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-25 14:18:57,605 - rubix - INFO - Centering stars particles\n", + "2025-04-25 14:18:57,632 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-04-25 14:18:57,633 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-04-25 14:18:57,635 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-25 14:18:57,635 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-25 14:18:57,636 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-25 14:18:57,639 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:46,740 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:18:57,658 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:46,763 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-25 14:18:57,676 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:46,781 - rubix - INFO - Getting cosmology...\n", - "2025-04-24 17:13:46,839 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-24 17:13:46,898 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-25 14:18:57,691 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:18:57,781 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-25 14:18:57,838 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:46,975 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-25 14:18:57,911 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:46,998 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:18:57,933 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-24 17:13:47,037 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-24 17:13:47,038 - rubix - INFO - Compiling the expressions...\n", - "2025-04-24 17:13:47,070 - rubix - INFO - Number of devices: 4\n", - "2025-04-24 17:13:47,075 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-24 17:13:47,201 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-24 17:13:47,209 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-24 17:13:47,247 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-24 17:13:47,248 - rubix - DEBUG - Input shapes: Metallicity: 5000, Age: 5000\n", - "2025-04-24 17:13:47,706 - rubix - DEBUG - Calculation Finished! Spectra shape: (5000, 5994)\n", - "2025-04-24 17:13:47,707 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-24 17:13:47,714 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-24 17:13:47,715 - rubix - DEBUG - Doppler Shifted SSP Wave: (5000, 5994)\n", - "2025-04-24 17:13:47,716 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-24 17:13:47,793 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-24 17:13:47,796 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-24 17:13:47,797 - rubix - INFO - Convolving with PSF...\n", - "2025-04-24 17:13:47,801 - rubix - INFO - Convolving with LSF...\n", - "2025-04-24 17:13:47,808 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 1 and noise distribution: normal\n" + "2025-04-25 14:18:57,969 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-25 14:18:57,969 - rubix - INFO - Compiling the expressions...\n", + "2025-04-25 14:18:57,973 - rubix - INFO - Number of devices: 4\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-04-25 14:18:58,170 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-25 14:18:58,264 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-25 14:18:58,269 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-25 14:18:58,295 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-25 14:18:58,296 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-25 14:18:58,551 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-25 14:18:58,552 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-25 14:18:58,557 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-25 14:18:58,558 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-25 14:18:58,558 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-25 14:18:58,589 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-25 14:18:58,592 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-25 14:18:58,592 - rubix - INFO - Convolving with PSF...\n", + "2025-04-25 14:18:58,596 - rubix - INFO - Convolving with LSF...\n", + "2025-04-25 14:18:58,599 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-04-25 14:19:14,194 - rubix - INFO - Pipeline run completed in 16.56 seconds.\n" ] } ], @@ -444,7 +447,7 @@ "#NBVAL_SKIP\n", "\n", "inputdata = pipe.prepare_data()\n", - "shard_rubixdata = pipe.run_sharded(inputdata)" + "shard_rubixdata = pipe.run_sharded_chunked(inputdata)" ] }, { @@ -479,12 +482,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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TOfZNRES+4WjfxCDLDuMwjOjoaHZkREQ+4E/D4ebMmYNVq1bh6NGjCAsLQ5cuXTB37lw0adLEdExBQQGeffZZfPXVVygsLETfvn3x/vvvIyEhwXTM+fPnMX78eGzatAmRkZEYOXIk5syZA53OsW6ZfRMRkW/Z65s4yJ2IiMhBmzdvxoQJE7Bjxw6sX78excXF6NOnD/Ly8kzHTJ48GT/88INpwfnLly/jvvvuM+3X6/UYOHAgioqKsG3bNnz++edYunQpZs6c6Yu3REREHsB1suzIyclBTEwMsrOzebeQiMiLAuHvb0ZGBqpXr47NmzejR48eyM7ORnx8PFasWIH7778fAHD06FE0a9YM27dvR+fOnfHzzz/j7rvvxuXLl03ZrcWLF+P5559HRkYGgoOD7b5uIHw2REQVkaN/f5nJIiIiclF2djYAIC4uDgCwZ88eFBcXIzU11XRM06ZNUbt2bWzfvh0AsH37drRq1Uo2fLBv377IycnB4cOHFV+nsLAQOTk5si8iIvJfDLKIiIhcYDAY8Mwzz6Br165o2bIlACAtLQ3BwcGIjY2VHZuQkIC0tDTTMdIAy7jfuE/JnDlzEBMTY/piZUEiIv/GIIuIiMgFEyZMwKFDh/DVV195/LWmT5+O7Oxs09eFCxc8/ppEROQ6VhckIiJy0sSJE/Hjjz9iy5YtsnVSEhMTUVRUhKysLFk26+rVq0hMTDQds2vXLtn5rl69atqnJCQkBCEhISq/CyIi8hRmsoiIiBwkiiImTpyI1atXY+PGjahXr55sf/v27REUFIQNGzaYth07dgznz59HSkoKACAlJQUHDx5Eenq66Zj169cjOjoazZs3984bISIij2Imi4iIyEETJkzAihUr8P333yMqKso0hyomJgZhYWGIiYnBmDFjMGXKFMTFxSE6OhqTJk1CSkoKOnfuDADo06cPmjdvjsceewxvvvkm0tLS8NJLL2HChAnMVhERVRAMsoiIiBz0wQcfAAB69eol275kyRKMGjUKAPDuu+9Co9Fg6NChssWIjbRaLX788UeMHz8eKSkpiIiIwMiRIzF79mxvvQ0iIvIwrpNlB9ciISLyDf79tY6fDRGRb3CdLCIiIiIiIh9gkEVERERERKQiBllEREREREQqYpBFRCZ6g4inv9yHT/532tdNISLya/lFJXji8934djcXhiYiSwyyiMhky4kMrD1wGa/994ivm0JE5NeWbjuL345cxXMr//J1U4jIDzHIIiKT4hKDr5tARBQQrucW+boJROTHAi7IWrRoEerWrYvQ0FB06tQJu3btsnn8t99+i6ZNmyI0NBStWrXCTz/95KWWEgUerudAROQYjeDrFhBZupFXhOe+PYBvOIzV5wIqyPr6668xZcoUvPzyy9i7dy/atGmDvn37Ij09XfH4bdu24eGHH8aYMWOwb98+DBkyBEOGDMGhQ4e83PKK6Z31x/HmuqO+bgapSG9gmEVE5AhBYJRF/ufj/53Gt3suYurKv3AhM9/XzanUAirIeueddzB27Fg8/vjjaN68ORYvXozw8HB89tlnise/99576NevH5577jk0a9YMr776Km677TYsXLjQyy2veLLzizF/wwm8//spZOVzyERFUawvHy7IdcqJiIgCR4neIMtgsSiLb+l83QBHFRUVYc+ePZg+fbppm0ajQWpqKrZv3674nO3bt2PKlCmybX379sWaNWusvk5hYSEKCwtNj3NyctxreIB565dj0AjAlD5NLPb9dTELy3ecx7N9GqNQMnenpJJnPy5k5iM6LAgxYUEOHS8NXozfimb7RIv9IkSx9LPWS74MoogSg4iqEcEIDdKioFgPgygiWKuBViPgVrEexXoRBkPpccbjjY/1Zl+/Hr5qatuQRVsxc1Bz1K8WCb1Y+lxRLM12FZYYUDM2DBdu5OO3v69icNskxEeG4EZ+MUSUvynp+zC+B+P7M71Xya+P8bjy7+WfS3xUCG4V61FYbECR3oBT6bk4mZGLh2+vjfxivek8AgBBAAQIpUN6yr4v3VZ6B1qnFRAdGoT0nAIUlhggCIBGEEzPEwTIv4d8P4TS4UKCIMj2oey1NWXbBdO/tu98GwwiCkr0KCg2oMRggN4gokRf9rMRS39mBhEwSH4WBtH4e1D6GSntN22TPF9+rPG5IgwGQF+2v1gvokRvQIlBlP0eWvvdtfhZSvbcf1stVI8OtfreiYjIfdtOXcc1yVzBlXsuYvJdjZl19ZGACbKuXbsGvV6PhIQE2faEhAQcPao8ZC0tLU3x+LS0NKuvM2fOHMyaNcv9BqP0wuXtX4+jdlw4RJRetOQWluBkei4aVo9EUYkB4cFai4shURRRVGJAkV5EkFaQXAhJLoZMx0Lxwtn42CCK0GgEaAQBolh22VN27WN8nvH4tOxbOH41FwCw/fR16DTyROf209cBAF/vvoDY8PKA4v4PtiE2PLjs3GVtlVxQazVC6ZcgQFP2b1pOAc5cy0PD6pGydhkvvqXnECXtlV6smwci0su9wmIDbhaWmF4/JizIdKFuvKiUvqbx52X8XiOUtjmnoBiiCIQHa80uSMv/NdJpBOgVsj+BmhA6cDEbQz9QvoFhbs7Pvh02+ua6Yz59fWdoBECn0UCnFaAr+79RUGzALUmQWNF0a1iNQRZVOP542VqiN+DAxWy0qhmDYF1ADVYiFfx8qPT69v72tfDfv67gcnYBTmXkomH1KB+3rHIKmCDLW6ZPny7LfuXk5CA5Odmlc13IvIWFm06q1TSv+vPsDZv7s/KLTd+fvZ4PXHdt3O/J9FyXnucMvUFEZp57Qxrzi+xfAPsio6fVCE7No5IGvKbvy74ybhbafK6mLDvjyPs0ZXRQnr0RZNsF0xWKNOsE0/fG8wim741Bs1F0qA45BfJtwTpNWeBcHogbg2dbwW6ITiMJukVZIK52kGwQgSK9AbZ+pYyBmEZT9q8A6LQaU+bM+LMwZs+MNwUE03ZjZk16bOlztRrB5nmM23RaATqtpjQQlN4FlX1b/jNT2G3aXiU82P0Pjsjf+GGU9e9fjuHDLacx7PZkvDG0tcvnuZpTAFEEEmN4cySQHLqUDQBIbVYdx9Ju4uClbJzOyGOQ5SMBE2RVq1YNWq0WV69elW2/evUqEhMTFZ+TmJjo1PEAEBISgpCQEPcbjNILjMc618GFG/mmO9a/HJa3JzY8CHc0qS4b2qQRBKzZf8k0JC9IK+CxznUhCJBdSBmHKGkEQFt2Z1yrKb07brxIEgDTcCKLi1fjHXWNYHruP7/ab2rbpDsbolFC6X9MAcCsH/7GtdzSC/HkuDBcyLxlOnZ013ro0qCqbIiV8cUM0iFpZcObTqbnQm8Q0a1hNWg0kmFVAmRDqyyGW0E+JMv8cWlbBRy/ehOHLmUjNjwYrWrGoHp0CASUXnQbLzCN70t6IW88h6FsWNz5zDycu56P7o2qITRIK7sYFQQg51YxUt/ZYvocdr3QW7HjNb8Ylf4c5I8lx5u9L2NgpBHkF8pNZ/yMguLS35Wht9XC7HtaYMb3h7B2/2XMf7gdUpslyI635kr2LaTM2WixfWDrGlj4cDvTcw9ezMaghX8AKM3eSYOurg2rYvkTna2+hjvqTvuv6fszcwZAEAS8uPoglu88DwCYNbgFRnapa/c8YllGdN6GE1i06SSWje6Irg2rOfQcpUxr6f6yTCskwZnBMtgzBm8lZUMBiw0GGAwiQoO0CAvWIjxYi1CdFhqWLSMiJ+UVluDDLaULyX/15wWXg6xbRXp0+r8NCA/W4q+X+0CnZUYsEBgMIk6k3wQANE6IQu2q4Th4KRsXbtyy80zylIAJsoKDg9G+fXts2LABQ4YMAQAYDAZs2LABEydOVHxOSkoKNmzYgGeeeca0bf369UhJSfFCi4HkuHC8OqSlbNsX289ixveHAQBdGlTFJyM7IDzY8sfQoW4V0wKHLw9qgUc71/F8gwFMXfmXKbh7tHMdJEiG+Ow7n4XPtp4BAPRrkYgvd11Abll2YWj7mmiRFOOVNjqiSWIUBrVJcvs8DatH2twfFqQ1fS8I8MmQqBJ9eZDz+r0tERqkxdsPtMFrQ1oq/m5ZE2ylIw3RamTBWZWI8qGiyXHhOHMtz/Q4LMhzf1IaVo/EyfRcBEvaEyRpc1SoY69tDOan3NUYU+5q7NRzyh4502wiIq9Qa+TMlezSi/L8Ij3yCvWICWeQFQgu3MhHQbEBwToN6lSNQHxkacLAeHOcvC+g/udMmTIFH3/8MT7//HMcOXIE48ePR15eHh5//HEAwIgRI2SFMf75z39i3bp1ePvtt3H06FG88sor2L17t9WgzBukF4WtasZYvQiOlhRRkM5/8jSDZGxUqE4r2ycbEiTJ/gDWL9ArOunPU2sjS+RJT/duBKA08A0tC/oEQXAqwAJgdfy+1iyrUjWiPNNrXuwjLFj+O6OmZ1IboVpkMOY/3M60LUTSZmffLxGROwQ/uuEiiiI++P2UOueSfF+or7hzRSsa45z6hvGR0GoEVIssHaZ9nUGWzwTUVclDDz2EjIwMzJw5E2lpaWjbti3WrVtnKm5x/vx5aCTFGrp06YIVK1bgpZdewgsvvIBGjRphzZo1aNmypbWX8DiN5ELc1tAt6V352DDfzGcwq3shW3hRKKvYVv7Yfzobb9JJPhRfDfEa060e6sdHILVZgv2DbbAWZOm08vclDaTCzYKq8CDPBVl3t07C3a3l2UlpkGveFiIibxFF0Wf94IXMfLy34YRq55PO8y2SVBIm/3b8qnGoYOkInGplmazruVxmx1cCKsgCgIkTJ1rNRP3+++8W2x544AE88MADHm6VE8wDFSuiQnyTyZIy7zCkjwWze3iVdQqJNMvjq88gIkRnEXy4wlo20jyTJdWhbhy2nbpueuzJTJYSaZAVEcIgi4g8q1hvQH6RHjFhQbI+3CACWh/1Afcv3oarOepkK97//STW7r9seswgK3CcKAuyjHPpq3K4oM8FXJAV6KSZLFsX5ZGSTJaj6y+pQTCVh7Bsn3lQJTiYlavIpO/bV8MF1WLtZ2heyh8AfnmmB85ez0OE2RC9UA9mspQE6crbzOGCRORpj36yEzvPZGLH9N6yPtEgitB6efigcVkStQIsvUG0WA6jSM8gK1AcKxsu2LgsyDIOF7zGTJbPVM6JND4kDVw0Ni7KpUO3YryYyZIuIGox3txGFq6yZrKkbGV8ApnS+2qSGIW+LRIthhJ6e8ieNPvm7QCPiCqfnWcyAQA//nVZtt18KY19529gzb5Lqr72tdxCfLzlNDLzilBYosfA+X/gic93q3b+vKISi23MZAWGEr0Bp8qWxGmaaAyyyoYL5hWaAnLyLt769TLz4hHW1IgORUr9qggN0iAqxDc/JvPmSYMujazamu2AsbKoqEGWzsb7CjILsry9+GWQLMjiPSMi8g2D2UXsve9vAwDUqRqOdrWrqPIaT36xB3vO3cCmY+l4qmcD/H0lB39fyVHl3ACQX2hZ5KKYmayAcPZ6Por0BoQHa1EzNgwAULUsk1VQbEBuYQmiQn0z9aQy41WJlzk6XFCjEfDluM5Y8nhHrw7FMw+kZG2yUV2QMVbFDbJsvS/zoYTeHjIpbZp5NUwiIk8xv9FobVH485n5qr3mnnM3AEA2D1ZNSpmsYj0zIIFg3/nS343GCVGmIlzhwTpElI0u4ZBB32CQ5WWCYD2I8TcWmSxpkIXAei/eUFGDLFuZLPPhgt7+DKTXNSHMZBGRB12QBEzmf+oMVhI+nuobbf1ddlXGTcu5XSUMsgLCr39fBQD0bBwv214tKrCKX4iiiNMZuVZvWgQaXpV4WSBV5DPvHCyGC0r3+fl78YZAL3xhjVah8IVRkFlFQvOgy9OkQ3RCmMkiIg/ZcjwD3d/cZHpsfkPJfLigkaeCLHs3tFxZu3LYRzsstpVYix7Jb+QXlWDL8QwAQN8WibJ9xnlZ1xQCaH+0ePNp3Pn2Zny4RZ0133yNQZaXObpOlj8wb518Ppl8HzNZvlsny9NsBU7md1N9mcmqqJlEIvK9ZdvPyR6b9996LxcWsPd68VEhNvcfupSNDzefMmUMrGUOmMnyb1/sOIfUtzejsMSAOlXD0axGlGx/eYVB/w+yCor1mLvuKADgvd/UW/fNl1j4wsscrS7oDywLX5QrLeFu/djKyBPDN/yBreDFPJNl/tjTWDGJiLzBvMiPCPlNHoPkgfTvkrVgaN2hK1ix6wLmDm2FGjGlhQq+3HUeNWPD0MNsyJeS7Pxim/vtlV6/e8EfAEqLFT3etR5yCyznYwHMZPmzHaevY8aaQ6bH999WyyL4N2aylIaC+pvfj2WYvo/24tJFnsRMlpfJK/L5rh2OsLkYsSAANopkVEYVIZPy/vDbLLY5MycrtVmC6m2yJSqU94mIyPNKzDI9BoMoGyIoDaakcZW1G0FP/WcvthzPwAurDgIATmfkYvqqgxjx2S5ZwGbN+OV7be53tPT63vNZAICcAuWgjYUv/NeusuUEAKBXk3iM7FrX4hhjRjMjAApf/HTwiun7G3lFDv0/8He8QvGyQC4WweGCtikt2htolIIkR6sL/mdMJ8RFBHukXdYMaVcTG46ko0NddUokExEpMY+V9AZRtk2W1XIiw25cSPhS1i3Z8zVuLmzsaJBlnLuVfUs5yCoxGPDebydwMiMX8x5qWyFuJlYUf18uLd//0sBmeKJ7fcVjTHOyAmC4oLF6JlB6UyP7VjGqePmaQm0MsrzM34tFSBcjNictdVH6vXThYqoInY/S76Sj62TF+CC9H6LT4qMRHbz+ukRUuZUYDLI77bLvnbgBbwzIpHOf1LiB7+j6VsG60r/ht4ot18gCSoO1d387DgB4tFNtdKpf1f3GkSpOXytdfLhB9UirxwRKkHUjr0h2owFAhQiyAv/We4Dx98IXtm7AmQ91lB7LTFYFCbIUttmqLqiTzMGqCO+fiEiJeRdXrBdlQwSlhSNs3aw0ZwyypEGRcZvBIGLm94ewYud5p9tbYhDx41+XkX6zwOZxxnm0xVYyXzm3yudqFZQYcOZaHufC+gG9QcTZ66VLCjSoZj3Iio8qDVL8fU6WcVHtOlXDUSMmFID17GogYZDlZdLrVX+8JrX1p9M8Cyc9VuBvUoUIMpQCf1uZrIhgLTrUqYJWNWPQOMH6H3oiokBm/lewRC8fLjh66Z+4XpYtcCYGMcZm0rlPxuf/dSkby7afwwurD7oU2ExcsQ8dX99gsV16LuNwwUIrmS/pXK1Fm07ijrd+x3IXgj5S16Ubt1BUYkCwToOaVcKsHhcfWRqwXMst9Ovg+PDlbABAi6Ro06gYBlnkNPO1pvyNrf+E0hLlGkGQHeuP78XbKkSQpbDN1vsSBAHfPpWCtRO7yrJaREQViXkXV2IwyOZenb6WhxdWlxaxcC7IEk3nMzJmyAokQ/jyi5SH87miUJK12n3uBv48m2kjk1V+oWsstPCSpKId+YZxqGDdquE2++hqZZmsgmID8lT8HVLb4bL5ZS2SYhhkkev8vbqgM/c5pMf643vxlrHd6yFEp8Hz/Zr6uiluU5yTZWeBYUEQ/HLoKxGRWgSzW1DFetFibalzZcO3nCl8YTxUPier9HvpxXNekXKJdUeYV2mTBk77L2ThgcXbrc7J+q+k4hv5j9MZeQCA+jaGCgJAeLAOEcFaAEB6ju2ho75kDLKaSzJZWQyyyFkVZ06WIDvWvAOqTF4Y0Ax7ZtyFjvXifN0Uj6gIGToiIndYZLL0BosCFcbgyJmblcZATXp+sSypJK0Q+PL3hx0+p/kQ71yzAO2jLactnpNlZd2tawFQ+ruyOZWRix/+ugwAqBcfYfd443DCizdu2TnSN/QGEWeulQaNTROjTEFWDoMscpZ5oBJI5EMd5UMLA+ytqEoQBESGVNxCnRV1kWUiIkdZFr4wWAyvNwZMzmSylI41bpMGWT8fSrN7rpiwIHwxpqPF6APzcu7bTl23eK61IIv8y8Ub+eg3bwv2la1v1jIpxu5zkquEAwAu3Mj3ZNNcln6zAHqDCJ1GQPWoUMSGc7gguUgaWAXatat8nSzBbLhggL0Zcpit6oJERJVRkcJwQeNj0bHq6aXHivJ/AeAfy/fiWNpN2dwpe2pVCcP+mXehe6N4BJn9zTYv5z6wdQ2L57szHJG85/djGaYiKXERwejSwH5J/eS40iDrfKZ/BlmXy0q3J8aEQqsRyocL5gd+FpVXT14WyJksjfl8MlF5HwUuZ6sLEhFVBpZzsiyHC+pFEX+ezcS0VX9ZPc83f17AdMl+UWGI4fbT13Hf+1tRWOJ4oQKdpnxurHkmq7jEfmYtt7A0yOreqJrDr6mmjJuFpsV1yTpjNuqetklYP7mHQ+tIGYOsi5n+OVzwclbpXLGkmNJhjRWp8EXFHePkp2QL+gbYtausk2Emq9LgnCwiqvQU5mSZ93sGA/DA4u02TzP1O3kAZuxHzYcN5hXpLYb52SL9O21e6bVIksk6cy0Py3ecs3h+XlmQFR8V4vBrqun2138DAKyf3AONEqJ80oZAcKUsIGmZFIOqkY79rGoHSCarRmxpufmY8NLAsSIEWcxkeZkmgDNZlosRc05WZcBMFhH5k293X8CW4xmmx7eK9E5lfVxh/lewWC9aBEbSMuyOKh8uaJltKrKydlVqswSLbTrJEMFgrfJwwXWH0nDHW7/jcrZllbncghLF53rbn2dv+PT1/d2VbHlA4ojkuNIMkb/OybpS9vuYFCvPZFWEeYIMsrxMttZUAH/6AuSZLH+slEjqYCaLiPzF35dz8NzKvzDis10ASteSaj3rF/R4c5NX21GkN0BvFhddzSm0OM5eDQxjoGY+9BCwLFhhpPQnWZ7JshzauO3kNTz1nz1W22EcLhis8+2FiehUbcbKxzi0rkaME0FWWeGLrPxiv8wOXSrLZCWVvSdWFySXCbLvA+viVTAr2hFYrSdX2Vsni4jIW4ylno1OpueiWC/iak6hRSEKNZnfSCydk2X/9ewdY224IACr70fpxleQ5O+0+eiDYr0Bu85m2myHsfBFEBeV91t6g4irOcYgK8zh50WE6FC1bO7WBT8cMmjMzhkzWbEVaE4W/zd5mSAE7pwsjXxKFlUSrC5IRP7Ccohe+WNn5jA5y7zLO3QpGxuOXLX7PHtxmHG/UjxVYiXI0igEWdLAyzxQKioR7c7HySvUKz7XE0oUyt+TfddyC1FiEKERgOpOzp0zFb/wwyGDV7LkgWOVsjlZeUV6FFhZJDtQ8OrJywK5uqAsCxdgbSfXcU4WEfnCmWt5FheF5v2m3ktBlrlruUUoKLb/evYyWTfyiyCKomLQUWJlTpbStYN0TpZ5oPTr32lYtfeSzXaYhgs6OHLB1axhZl4ROs/ZgOdWWlZYBAAtry2sMhaISIgOtShuYo+/lnEvKNbjel5pqfaksnlm0WE6U2bWuC9QMcjyMumfj4ALsqRZOB+2g7yLc7KIyNtuFhTjjrd+R7e5m2CQXNBLry1FUZStAeVK4QlHuZp3sZew0RtEnEjPRYn5BC8Avx1JV3yOUhxka07Wkq1n7bbTVPjCwTlZxuOdtWLnOVzLLcLKPRdN26QBm1KWjkoZC0Q4Mx/LqLax+IWflXE3vqfwYK1pLpYgCIgvq5yYcdNynmMgYZDlZRVlMeJACxDJdcxkEZG3SS+upFX2pH2P3iDKhhN5cEqWyxwp5NDn3S34v5+OWGzffyFL8XjFTJYksDJfjNgRt4rLhwu+NLCZ3eNzCkrnyzy/8i/0fvt3Uwl4e8yTc0UlBkyVZLU+33aWQwmtMGZ1qjlYul3KWPzC3zJZpvLtMaGyG/nGpQTScywrYQYSBlleJv3bGGhD7uTDBX3WDPIyZrKIyNukw9/yi8oDKWmAsf30dYxa8qfpsScvzl09t6OBn7X5V0rszclyp1hRkFaDJ7rXx5k5A2weZxxe+PXuCziVkYftp647dH5pJvLfvxzFl7vOY9W+8qGMhy/n4Ne/7c91q4yy80uDrNjwIKefa1wry9/KuBuDLGPRC6N61SIAAAcvZZu2nb2Wh1FLdmHAe/9DmsIyBP6IQZaXSSsKBtq1q7y6YIA1nlymY+ELIpMtW7Zg0KBBSEpKgiAIWLNmjWx/bm4uJk6ciFq1aiEsLAzNmzfH4sWLZccUFBRgwoQJqFq1KiIjIzF06FBcvcoLS6NfD6fh3d+Omx5L5yZJg4nHPt0le54/ZrIcqUDoLKV5S7bmZDnDOFzQ3k3gnFvFyJasY2RtTS9z0kJKizadMg0XkzqZnit7rDeIAZ/RUIOx2l5sWWEIZ5gKX2Tekg2/9TVrJem7NKwGAFi+8zy2HM/Ax1tOY8D8/+H3Yxn4+0oOlm476+2muoRXT14WyEPu5Fk437WDvEvLEu5EJnl5eWjTpg0WLVqkuH/KlClYt24d/vOf/+DIkSN45plnMHHiRKxdu9Z0zOTJk/HDDz/g22+/xebNm3H58mXcd9993noLfm/cF3uwWpLd0Du48L0nApry13Xt76C0SWpl2kKDLC/dpMO63Rl9IF2M+B+9Glg97rmVf6HN7F+dPr/58HOl4ZTmQcBLaw6h4/9twI7TjmXLKirj4rzGuUvOqBETCp1GQJHegKs3/SdgNRa2qVU2nNHonrZJaJEUjcy8Ioz4bBde/+mILKO96ajyfEV/wyDLy2RBVoB9+tIsXKANdSTXcU4WUbn+/fvjtddew7333qu4f9u2bRg5ciR69eqFunXrYty4cWjTpg127SrNumRnZ+PTTz/FO++8gzvvvBPt27fHkiVLsG3bNuzYscObbyVgSK+5bQUQngyyXA2QpM9Tax2v+9snW2xT62ZYkK78PFP7NbV6nPncHvNX3302E2/8fBSFJfIS3BZBlcJH8vb641iy9Yzp8Ze7zgMA3vvthI2WV3xZt1wPsnRaDWpWKR2Sd+66/wwZvHijdLhgcpx8uGCIToslo25HSv2qqBIehCYJUfi/e1th90upEATg2NWbpjXD/FmAXeYHvkAOVORZON+1g7yLc7KIHNelSxesXbsWly5dgiiK2LRpE44fP44+ffoAAPbs2YPi4mKkpqaantO0aVPUrl0b27dvt3rewsJC5OTkyL4qC2lmw9YIEH+slyCNq/QqNbBRQqTFNunNMFsBoa3sFAAEa7Uutcl8Ttn9i7dj8eZTeH/TKdl28zL7H245rXi+WT/8bdk2BysfVlTGIi8RIa79jGr7YRl34xyxZLNMFgBUjw7Fl+M6Y9/MPvhlcg880qk2qkWGoGVSDADgjxPXvNpWV1Tu31gfkGavAm64oOz7wGo7uY6ZLCLHLViwAM2bN0etWrUQHByMfv36YdGiRejRowcAIC0tDcHBwYiNjZU9LyEhAWlpaVbPO2fOHMTExJi+kpMtsxkVlTQDZCtO8UQmKyu/CH+ezXSjhHv5M9WqMK/VCFg/uQfqlxUHMG4zvaaN5zZKiERSTKjVbEiQixmxQitrlB28lI0TV29ixGe7MH/DCax3sqjF3HVHTd9X9iDLGKC6OufOVPzCT4KsG3lFpkxWPcnvsj1dGlYFAOw+d8Mj7VKTztcNqGxkmSwftsMV5pksP7xpSB5Q2Ts2ImcsWLAAO3bswNq1a1GnTh1s2bIFEyZMQFJSkix75azp06djypQppsc5OTmVJtCSBk+2SqKrPZ//xNWbuOvdLQBcH73xyg9/45vdF/HFmI7Y5mAFPnu0goBGCVF4eXALjPysdBiqozfDIoJ1+PHp7ijRG9Dx/zZY7A9y8e+93koEKYoiHv54B67lFmHL8Qynzvn+7yfxwe/lmbDK3hcZs4XuBln+ksnaeSYTANCoeiSqOlGWvl1yLADgr4tZHmiVuhhkeVlAF76QDXX0YUPIa4a0TUJ4MP9MEDni1q1beOGFF7B69WoMHDgQANC6dWvs378fb731FlJTU5GYmIiioiJkZWXJsllXr15FYmKi1XOHhIQgJMT59XEqAlmQ5cVMljHAKj236+f5+0oO2r/2mwotKmUs4S6Nq3QOXngH6zSIi7BenS7YxQt4ayXoDSJwLbfI6fPFhgfhzXXHZNtC3KiaWBEYF952Ndvob0GWsZBJ5/pVnXpew+pRAErnlomi6NdTbyr3b6wPaAJ5XpPZGl+B1nxy3rxh7XzdBKKAUVxcjOLiYmjMqhpptVoYyu70t2/fHkFBQdiwoTyLcOzYMZw/fx4pKSlebW+gkFYHtxXrqLlOlnSRY38iHRYoLeXuaCYrRGd7Po9S5UJHWCsLvtnJ7JVRlqQ8vFFlz2S5O1ww2U+GC67aexH3LNpqKsPeqX6cU8+vVVbAI7ewRPH3xJ/wFrXXBXDhC7PvOVyQiCqb3NxcnDx50vT4zJkz2L9/P+Li4lC7dm307NkTzz33HMLCwlCnTh1s3rwZy5YtwzvvvAMAiImJwZgxYzBlyhTExcUhOjoakyZNQkpKCjp37uyrt+XX5Jks7wwXtDbHSE1RoTrcLChx6jnSwEp6DSGbk2Xjc5AGKi1rRuPQJXkBFXtBmDVqVU60xZ1FliuC8kyWi8MFq5YGWddyi5BXWIKIEO+HAKIo4v9+OopruYWmbd0bxjt1jtAgLRKiQ3A1pxDnM/NRxUZm1tcq920BHwjkCn1cjJiIKrvdu3ejXbt2aNeuNMs7ZcoUtGvXDjNnzgQAfPXVV7j99tsxfPhwNG/eHG+88QZef/11PPXUU6ZzvPvuu7j77rsxdOhQ9OjRA4mJiVi1apVP3k8gkBW+sHHckSs5uFmgzp1tbwQNt9Wu4vRzpElSaWClc7DwhfQ53zyZgm+elGdPQ1ydk+WFu64lZi9SVGLAmWt5nn9hKwpL9Bj20Xa88fNR+weroHxOlmvXX9GhQYgNLy14Yqzq520Xb9ySBViD2iQhJtz5kvTGaoT+MvTRGmayvEwanGgCLMqSZbICq+lERKro1auXzWxKYmIilixZYvMcoaGhWLRokdUFjUlONtfKxsX8P7/ajxoxodg+vbfbr+mNIMuVUtxaWfZKst3BhTelzw8P1qFDHXmgFxrkWibL2nBBNRWbBVmzfjiM5TvPY8HD7TCoTZLHX9/cTwevYMfpTOw4nYlp/a2vKaaWYjeHCwKl87Ky8rNx/no+miZGq9U0hx1NuwkAaF4jGvMfbou6VR2vKiiVHBeO3edumKoT+itmsrxMGpsEWIwV0OXniYjIv2w5noE73vrdNAHeGoMsxrJ9MX8lW50FSr0RZIUFOX+fW2NluKCj62SZD7nTaARsee4O02PXM1mODel0R4lZBcPlO0sXKV606aTS4R6Xll1o/yAVFendqy4IAEkxpfOZ0ny0kO+pjFwApUsJNKwe5XDBFnPGeVm+ysg5ikGWl0ljk8CbkyVtvO/aQUREgW/8f/bgzLU8DPtoh83jHF0nS01qLRxsS1iw85dgGmuFLxwcQma8OJVKjgvDXc0T0LNxPOKj5BUs68c7lmmQ/oysVRp0l/lwQSNf3fTNyne+aqI7jEGmO0FWYkwoAPVuRjjrVHppkNUg3nJBbWcYhwsyk6WSzMxMDB8+HNHR0YiNjcWYMWOQm5tr8/hJkyahSZMmCAsLQ+3atfH0008jOzvbi622pAngeU2BXH6eiIj8S16RYxX8HC3hria9FyYZBWtdGC4oDbKszMmy5oPhtyEq1HL+iyAI+HhEB3w+uqPFzd8fJ3VDsxr2h5VJhwsWeahoiLHwgyiKsmyZqxUR3eXJCpTFeoNFRrB8uKDr11/GIOuqr4KsDHWCLOPNgot+PicrYIKs4cOH4/Dhw1i/fj1+/PFHbNmyBePGjbN6/OXLl3H58mW89dZbOHToEJYuXYp169ZhzJgxXmy1bYE2XFAqkNtORESBw+Bg4Qtr9l/Iwtx1R3HLwaAO8E4my5WS5PLhguXbpXOyrDXdWF3OGeHBOnww/Da7x0mzV//30xGnX8cRJYbS4GrYRzvw0Ifl2U9XKyK6y1O/IwXFevT69+947NNdsu3FKgwXTIz2XSZLFEWcyigtVNKgumtzsYyM5egvZt3yynxAVwVE4YsjR45g3bp1+PPPP9GhQwcAwIIFCzBgwAC89dZbSEqynPDYsmVLfPfdd6bHDRo0wOuvv45HH30UJSUl0Ol889YDORskvcMlQPDaHUUiIgp86TkFCA3WIrosm9KoeiROpFsfkWLk7nyfIYu2Aii9OJ1yV2OHnqM3eL6EuytBltaB6oLmc5eMXF3dsm61CLz7UBtM/vqA1WOk2UbjXCm1FesNuJ5XhJ1nMmXbfVXaXT6MVb1FcQ9cyMKlrFu4lHULxXoDgrQaiKKIYhWGCyaUBVlXfTAn61puEbJvFUMQ4HLBC6MaMaHQagQUlRiQkVtoel/+JiAyWdu3b0dsbKwpwAKA1NRUaDQa7Ny50+HzZGdnIzo62maAVVhYiJycHNmXmqzdhQoEgVy0g4iIvGvPuRv4fv8lAMCNvCJ0/L8NaP3Kr6b9SjcalYIog4OLEdtjHKrkCL3nYywEOxgcvDSwmel7rZUpB9KAq0lClOJ53LnmsFe90BuFQkr0InKdXFfMkzw1D0067y6vsMT0Wsb/Gu4MF6whmZPlqQIlSn77+yrufOt3AEC9ahEuV7E00mk1pqzcRT8ufhEQmay0tDRUr15dtk2n0yEuLg5paWkOnePatWt49dVXbQ4xBIA5c+Zg1qxZLrfVHlnhiwCrHiH7Ay0EXpBIRETqKtEbrFYIG/rBNgBA/WqRuFlouX6VtFKgMROgdK2q1pysYCcyANayQWq67OCQrSaJ5UGT9AJcY6XwxbN9myAkSIsgrYBFm04pHq82Tw6vvKNJPDYdy0CJwYDsW+qsg6YGaWBVohfhZtxgIp3TdqtYj2vpuYiULBysRuGLW8V65BSUICbM+TWqnHX2Wh7+sXwvisruXHRtUE2V8ybHheFS1i1cyLyF9nVUOaXqfJrJmjZtGgRBsPl19Kj7i7zl5ORg4MCBaN68OV555RWbx06fPh3Z2dmmrwsXLrj9+lLSwMrBZS38hqztAocLEhFVZtNX/YV2s9cj/abtYOHM9TxZBsZI2oe0e3U9vth+FusOWd44lV/Au97xOJMB8EKMhYMXrRfiql+tfDhVeHD51bt8HpZ0uGD5BUV0aBBeGNAMLZNiZOd0J8ayl/VQc16MtAJiarPqeKRT6RV0sV70qyDLIMtkqfcLY8xeAcCCjSeR+s5m9Hhzk2mbO0FWaJDWFFileWle1qJNJ1GkN6BmbBie69sE/+rbRJXz1iqrMHjBj4tf+DST9eyzz2LUqFE2j6lfvz4SExORnp4u215SUoLMzEwkJibafP7NmzfRr18/REVFYfXq1QgKsh21h4SEICQkxOYx7pAOswu8OVmS733XDCIi8gNf7iq9Cbl8x3lMtjHX6ZfDaXi8S13TY6X5K1n5xZjx/WHF50svZt25lnfm4tQbmaxnUhthzOe7FfdJP57mNcqDpbzC8uId0usJpeqC5pcY7gzzt3dT1Ti8skSFcZaPda6DOT+X3mDXCILpvVnLZBV7Y2ynAvNMllryJQVaVpTNbyuSvEd3hgsCpUMGs28VIy2nQJYl9YRruYX4fv9lAMD8h9uhvdni1+5oWL20QuFfl3xbNdwWnwZZ8fHxiI+Pt3tcSkoKsrKysGfPHrRv3x4AsHHjRhgMBnTq1Mnq83JyctC3b1+EhIRg7dq1CA31g4lxsiDLd81whWxOVqA1noiIPMLeRe5//7qCAS1rSI4XEaxzIqskXYzYS0GWwQtDNe5sWh2fjuygGGhJb8JKS5RLM0rW5mSVM9/mer9tbxHo/KISHLqUjZqxlutwKdFqBKvzuMyXujEOhbx44xZyCiyDrEIPlYw3J4oivt9/GW2SY1GvWoQssFJzTlZekfV5ZzqN4HaBjYToUBxNu+mVMu6bj2WgSG9Ai6RoVQMsAEipXxUAsOP0dZToDdhxOhOTv9mPJ7rVw5M9G6j6Wq4KiAFrzZo1Q79+/TB27Fjs2rULW7duxcSJEzFs2DBTZcFLly6hadOm2LWrtORlTk4O+vTpg7y8PHz66afIyclBWloa0tLSoNd7bm0De6RD7gJuMWJmsoiIyIwjmQRpBsLZIgmyKm5uDBdUDkSUqZmZsEYQBLSqFWNln/w4I2mrZMMFFbIbamay7Pnqzwu4e8EfWL7znEPHj0gpHQLYsHoklj5+O+pKystrzNYCMw6FzMovxoVMy8VnjXOYruYU4Ktd55FbqG5xjBK9Ae+uP45nvz2AZ77ejwc/3F76upLfezWD8jwb7XdnqKBR9bIFpzNyC90+lz1/ni2tBNmtoTrzsKRa1oxBdKgONwtKsHLPRTz66U5k3CzEnJ+PokRvwKQv9+GNn92fcuSOgCh8AQDLly/HxIkT0bt3b2g0GgwdOhTz58837S8uLsaxY8eQn186NnPv3r2myoMNGzaUnevMmTOoW7eu19oupQnoQCVwA0QiIvKMYgcCEunwO2Og5Ohl6daT11A7LhzNk6LdymQ502t5Y50swPq0AWvbpc2yl8myyGO50W87+nG89etxh45LqV8VT3Svj4SoEOi0GujFQ6Z9smkVGgEFJeU3xveeu2FxrqISA7aduoZnvzmAK9kFOHb1Jl4e1MKxBjvgvQ0nsGDjSdPjjJulwUmhpF2qZrIKrScC3B0qCMA0JyvHC/PbjEHW7XXjVD+3ViOgZ5Pq+OHAZUxbdVC2b/3fV/HDgdJhiuN7NkBMuOcLfCgJiEwWAMTFxWHFihW4efMmsrOz8dlnnyEysnzF6Lp160IURfTq1QsA0KtXL9Oq4OZfvgqwALO1pgIsUJHfWfNdO4iIyH84MlwrPaf8rrnxetTREtJf7DiHAfP/h5yCYrdKuDvDGyXJAefnZsuGC0qu4JTnZMm3udNtqx1zajUCasaGmSpTSqfASQNGrQB0kAwzO5eZZ3GuE+m5eOTjnaYFdn84cEXxNU+m5+Kd9ccVhxzaIg2wpAqLyxutV3VOlmczWdFlQZani4jkFpbg9LXSn1fb2rEeeY0BLcvrMjRJiDJVEN0tCcbP+7AwRsAEWRWFYOX7QCBfJyvQWk9ERJ7gyHDBhZvKL1RdXZ8n42ahW2v7ONNteS/IUt5u7W1KN8szWZaXc+andqffdvbTeKRTbZv7zed1S4fbSYNDjUZAVGiQaa5X+k37Q9yu5RbiVpFlNujeRVsxf8MJu0PI1h64jMeX7EJWfhEAoGpEsMUxhSV6WYZNzUIpuTYzWe5ftsd4Kcg6fCkboggkxYSiWqRnCsr1aZGIxzrXwcBWNfDFEx1NhTz2X8gyHXMpy3dBVsAMF6woAjk4kWXhfNgOIiLyH85WlHM1fHG2TPgHv5/CHskdbUdHj+gNIo5cuenUazkrqWy9Imttsjp/TPIRyBcmtjzUPO7yZAl3c/bWJDPPvEmDWqUqzNFhQbiUdcvhjNqkL/fik5G3y7bdLJvrtOPUdZvPffrLfQCAj/93Gn2aJyounHvwYjYOXcoxPVZzTpbNTJYTBWOsMQ0XdDKj56yDZVX/WtZUnneoBq1GwKtDWpoeJ0SH4uClbFmQdS23yGOvbw+DLG+zssZFIGAmi4iIzBU5G2SVHe7sZWmJQXRq2Nrcda5Nep++6i98s/uiS8911I9PdwdgPZOVGBOKv6/kKO8sY21hYiPB7HaoW0GWk8crXd+0TY41Xfyar5tWr1qEKUsl3Wf8PtjJuUi/HZEv+yNdS8n4uRUU6xGk1Vi9Fvt290XZYs5Ss374W/ZYrTlZeoNoe06WCguseiuT9VfZOnCtrRR38YTEmNKMmTRov+7DIIvDBb1MejcoRBdYHz/nZBERkbnMPOcuYkwVAp28Lj13Pc+t6oKOdlueDrAAIK5sCJrSBX54sNb6MELJ97JjHKjg7tY8cCc/dqU5YrfVLp9bZT5c8J2H2uKetklYM6GrvIR72XHuDJMTRRHDPtpRfk6hNFvU5Y2NuH/xNtmxZ6+Vz/myNTTRfAFuNapRTlyxFw1e+Am/Hblq9Rh15mSV5lc8FWSV6A2Y/PV+rC0rPHGbyqXbbUmIslyq6Xqe56soWhNYV/kVgDS61qnwn8WbGGQREZE5pQtMW8PLXB1ZNW3VQfcKMPhhv6WUgQoP1lmdEyb9XKUBmiOrZLkzeMbZaotKwaO0Mp55EFYzNgzvDWuHtsmxZiXcS/8NduOmdF6RHpeyyku/CxCw++wNZOYVYd/5LNNnfatIj15v/e7QOa/myC/cXZ3Dl19UgiVbz+DQpWz8+JdywQ4pNYcLZue7HmQVlRisDjf84+Q1rN53CQDQrEa0RyoLWpMQoxBkcbhg5REXEYxeTeKh0wio4qOSkq6SDj3gcEEiIpLSG0RczrqF5Lhwm8GQq3FSVn6x6lXufE2pK9Vq5AswS0m3my/aa3lu8+qCrvfbzgYRSpks6Vpe5pksKaU5Wa5kcAwGERqNgMJi+fA7QZD/Dk777i/8+4E2skDMWa6W/F/8+ynMt1K9UIlOheGCxuqCNwtLTJ+RM0RRxEMfbcfhyzn4YPht6N0sQbb/dEZpNrBpYhTWTOiiSvbNUYnRlkHWNS+sB2ZNYKVSKgBBELD08Y74ZOTtAVfCHWaZLHeGbRARUcUy+4fD6P7mJqw7lGazEIBxn7M9yNDbauHXv9Ncbl9uQQkmLN+LlXsu4p1fj+FURq7L51KLUnCkEQSrn5+037UbZJk/duOSw9nCDkrVDqUBgvmcLPlzLd+XKxfqxup/5ksMaARBtuDvt3sulh1nfS6UPa5mspwJsAD3MnpGxkyWKJYXA7HlxNWbmLryAMYt240NR67iVEYu9p3PQlGJAc98td8iiDl3vTTI6tWkOkJ0lkVDPCkpVmm4IDNZFABY+IKIiMwZLy8/334OAPDWr8dwZ9Pq1o938f7cd3sdmysliqLiTczlO88DAP57sHRY1gebT+HE6wNca4xKlPrShY+0w7vrTygeL/3s5MGI5bHmp3an2w518mJZKSaSDhe0VfhL+rMzHufKHPaHPtyBleNTLIMsDZCeU2BxfEGx62XY1ZiT5Qg15vKH6LQIDdKgoNiA89fzcTLjJga0qiELiERRxKq9l/DzoTRsPHrVlEHdcDQdj3QsL89/s7AEq/ZexLgeDUzbzpUVGalbNdzttjqrRkyYxbbrzGRRIDAv4e7O0AMiIqoYlOZf2cp8GLMx7qx5ZYujld6KvXRhbIt5rPFwx2S0rxNnfU6WlecqzfG2qC7oRp99T7skdG9UzVSwwx6tRoOPHmsv2yZto60gS7pLayp84XzbD17KRpOX1mH4xztk2w9dysErZtUBP9pyymJYoTNcLeHerWE1p45Xq2BadGhpNmvKN/sx+esDeO83eVD/7Z6LePbbA/jtSGmAdVfzBCREh0BvEPHFjtKbKca1y/adz5I999z10iCrtg+CrIgQy9zRjfxip5eZUAuDLHKYrJCRIHC4IBERKfYENudkuVZc0GEtX/4Fe85leujscs5eJJuzNm2gQ13limyilUV7leaimJ/ancIXITotvhjTCU/1rO/Q8TqNgD4tEi22GdkOsiQ3dMu+dWdez+Vsy6yVuf/76ahscWFnuVrC/YykmqEj1BguCJQPGTyRXjpk9v3f5aXq/3fiGgCge6NqWPdMd3w8ogNeHNhcdsz4XqXZK+mw2xK9ARdvGDNZEaq01VWta8WYfs98NWSQQRY5jNUFiYjIETYzWR6+P1dYYsDUlX959kXKqL3QqjGDNeGOhnhxQDNseLanbL/5Z7fg4XZ4bUhLxayB5Zws9ztuR6cKKFcXdCGTZVwnywtL3rgyXDCyLHOycOMJPLh4u82FhM1tOpbudLENteY4GYMsKWnbD18uXeNqTLd6aJoYDQDo2yLBdO0XExaEno3jAQBnr+WbMkVXsgtQrBcRrNMoBv7eMOnOhgCAZ/s0QXxk6bpZaQ4E2p7AIIscZm+SLRER0cn0XJyXLP5qzhujILw14V7trtA4qik0SIuxPeqjQXykbL958DqoTRIe7VzHStvkjXMnk2XtnNboFIb3SbfZKnyhNCfLGxXqch0oAmGuUULpz+fPszew62wmVu6xP29QFEXM+fkIHl/yp8W+5DjLOUVSOSqtbRUbbjns8/Dl0sWvcwtLTBm2FknlNxFCdFrMHtwCUaE6zB3aGjVjwxAapEGR3oALN0qDxbNlRS9qx4U7XbVQLVPuaozDs/qiZ+N4JESXBllXFebgeQODLHKYvXUPiYio8lHKTD1sNg9GyjiyypMZrfBg71Y1s+bFAc1M33coW5Q1OtT1mmPOfGQWhS9U6LkdvW42BkcPlxVJGNI2CUEaxzJZWoUbut7IZGXlOzekrFpksEWp+qIS+9mwg5ey8eHm04r7miRE4cuxnfHzP7sr7j9wMcupNlqjtITQ3nM3cO56Ho5cyYEolg5BjY8KkR3zWEpdHHylL/q1TIRGI6BetdIg83TZkEHjfKw6cd6fj2UkCIJpblZCWTaNQRb5P4W1K4iIqHJTuvDPsrHQqWgq4e65KCvMS0GWvZ4wOqw8oJr/cDuM61EfayZ0tXr85Lsa2TyfM4GpRdtU6Lalff/z/ZrivWFtFY8zBh8vD2qOz0d3xBtDW8szWbaGC0quTI3HBVvJZE25qzEAoH415+f/mM+n+7+fjgIAujasKtt+9o2BFsGGefuMIhUKL5i7VWR97leQVoOUBlXRNDFKcf/0/s0UtzurikIBkzk/H0XPf/+O578rHWrbsma03fPUjy/93I1rYxnLt9fx8XwsI2OQleajIMuh2yn33Xef0ydevHgxqle3XsKVAo/0LhhjLCLyZ+y3PEtWGdDJlJSjh//0dHcMmP8/p85tFBrkH8MFpUXNasSE4oUB1i+SuzWshlpV1MsAeGK4oPQcSmsSGRnXyQoN0prm7jhaXVCQZbJK/1UaLnh73Sp4uncjDO9UG1/vvoA31x1z6D0Yzb2/Nbq+sdFie3RoEL4b3wVPf7nP9POy9jtr/j6Uqjyas1Ukw/g+rQ3LbGIl+HJWrCST1SA+AqcyygtwGAOm5kn25xsah7Mai1+cLctk1a3mu0yWVGJMWZCV7Zsy7g4FWWvWrMGDDz6IsDDbY0WNVqxYgdzcXHZWFQwLXxBRoGC/5VnS60Rn81GOBlnNk6IREaxFno07/9bEKcw58QR7Q/D0VqoBKp5L5X7Vcp0s919Aeo4grcbqHCHzYXQAEKSwyLAS2fxv45wsnfz4hzvWxjOppVm/qpGWWaY+zRPw699Xrb7GsNuTrZaFLyjWo32dKtg67U7JVuVfWvNFlx35hG8WWJ/7pTSXTSo0SJ0BaLFh5f8/hravhe/3XcblrFuyxYlbO1DUpYFZJuvE1ZsAgHouZBY9oVaV0r//5zOdq+KoFocHBs+fP9/hzmflypUuN4j8l/lixJ6uEEVE5A72W55jbR0nRzgzTLBrw2o2L5atiVWYc+ILBic+JzWCINn5zB6rk8kqP8mtIj36t6yBl9ceVljw1/LFpG/P2vA/83aaqguaHT/nvlayx00S5Bkee8P2Hrw92WoblJZPk17vDLs9GV/9eQH3t0/G8bKgovy59n/etgpsSNs0ums9HLuag7AgLX47kg5AvYIu0jlZTRKi8N+nu0EQBCzefAr//qU0I3hbHeVlBKSMmaxdZzPxwOJtpkyWWhk3dzWqXtqOY2k3rS5S7kkOhcSbNm1CXFycwyf9+eefUbNmTZcbRf5JthgxM1lE5MfYb3mWq4uvApJ1sjx4o87VdYucZX+4oOPtULsYmycKX0jjktzCEsSEB2HtxG4WxyllsqQ/b/PMlJRGobqgvcIXdzSpjkFtkspf305GKDxYa7Viob3AeGq/pvjwsfZ4tk9ji+GCjvy8cwusz1eUtnvmoOZY/kRn2QK7ISplsqpHl2f/GsRHQqfVQKsR8FhKHfRqEo8pdzV2aOHpetUiTL9nf569AQC4u3UNVI/yTfl2cw2qR0CrEZBTUOKTeVkOZbJ69uxp/yCJbt0s/8NR4JP+wdYIAgMtIvJb7Lc8S3rBfOJqrnyOlh0GUURuYYlDQZarGbNivfNrHnmCM8Go+l2q/Ixq9NnSQK1v2WLDSsGh0pwr6Seh01gPFuRTExwr4a7RCFjwcDv0aFQNNWPD8N+DV2weHx6ks3pOpd856Za4iGDTezcvRe9IcG8rk6XUJukrqJXJalMrFv1bJiIyRIc6kjXWokODsPTxjg6fJyJEh4c6JOPr3RcwpG1NTLmrMZJ9WFnQXIhOi3rVInAyPRfH0m6iRoxjw8fV4nId0fT0dKSnp8NgkP8ha926tduNIv9kXsKdwwWJKJCw31KPNHi4VazHsu3nHH7ub0eumiq52dOweiQ2HE13un3FSmO+PMBe3OJcJkvl4YIWc7LcP6d0qKdxfpDSeZUyWVLW5kMBZpkshcIXfVskWH3uAx2SAUBxiGmrmjE4eKl0kd2wYK3VNjRPsqyqZ+0mgnnGTO9AcC+d91QtMgT33VYTH20pLemuFGRJf5VDVCplr9Nq8MGj7VU515z7WuGVwS28VmzGWU0SokxBVq8m3p1z63SQtWfPHowcORJHjhwx/dIJgmAa66jXOz9BlQKD0t0lIiJ/x35LfeYZmkWbTjr8XEcDLACY1LsRPtyivKaQLSV+kslKaVBaDtyRLlP1whcWj91/AemPvTxbZXlexUyW5Lm2riGkzzV+L409gh3I5igFUNdzyyvMhQdrrbbhub5N7J7fyHzulyOxfW5Z4YtnUhvhmdTGWLHzvGmfUrulwxfVCrLUJAiC3wZYANCsRhT+e/AKDpUttuxNTgdZo0ePRuPGjfHpp58iISGBF9uVCudkEVHgYb+lPrNkoNU+QRDcG/UQGaLDjLub49Uf/3bqec4MF9x68hrqVYtAUqwLQ4ls/C69NLAZWteKxQ8Tu9ksd15+Kuvnql8tAqev5aF7o2pWjzFnnhlTY86XQSHIUs5kWQYDjhY8kbbTWEBD+l5sFc0wUsoIScurW1us+sdJ3RQDBmstjzJbWFpv/h9DgbG6oDFAkwZOSp+bNBvKv13Oa1e7tIDHvvOlc8b2nr+BpJgwU3l3T3I6yDp9+jS+++47NGzY0BPtoQDBxYiJKFCw31KfeSbLWp8QpNGgyM2skivBQbGDw/Q2HUvH40v+RLXIYGyY0gtD3t/q1OtYa9rBV/ogKrS0glurWvZLYds6FwD854lO+G7PRTzSqbbjbfNACXdpoGT8mSud1V4myxb5OlmW57FXBAOQB1ShQRoUFBvQr2Ui2tSKRWJMiNXPoqWVsuXW2m78GRs5MyfLGKDVlZQ7V3pvjlQsJOta14qBIAAXb9zCN39ewNTv/kLD6pFYP7mHx4NWp4Os3r1748CBA+ysKiXJ3RQftoKIyBnst9TnaJCl0wpwYZkrGVsL11rj6HDBDUdK5+5cyy3C78fTceaaOuvpmF98O8LWzcuk2DBM6t3IqfOZDw9UI5Ml/bEb513VrGKZAVSq7temVqxDryGfk2WZybI330v6PAD4dOTt2H8hC2O61VN9WFuE+XBBK+MFS/QGU+CXa8pklf6OJMeVf35KvwPuLJdApf8XO9aNw84zmZj63V8AgJPpubiaU+jxbJbTQdYnn3yCkSNH4tChQ2jZsiWCguR/SAYPHqxa48h/MZNFRIGC/Za6thzPwLnrjgUjrgRI5ly521ziQuGLvELno0E1u8JAWIxYWgDC+LMN0Wnx9+y++H7/ZUxfdVC2T6p21XD88kwPVImwHYDaGy7oyO+U9CZAu9qx6NrQ/jDLN8zW3pKyVvgiwmzYoVLWacGGE/hg8yl882QKWtaMMRW+iCzLZMWElX8eBcWWv4OFJZwz6q4RKXWx80ymbNulrHz/C7K2b9+OrVu34ueff7bYxwnEFZvsbwdjLCIKEOy31DXis10W26xV5NYIArQawa278eZlsh3h6HBBKWcWSTZSo5iEkdICvmpRK4CTXQZIThoerJNldaxlmxxZpFYeUBlfS/q69tspDbJCHSiUMb5XAwzraH0oZu9mCVi975LF9jDzIEvh9+7t9ccBAO9tOIGPR3RAbmHpOlnlc7K0Np9fWOwfRVwCWd8WCWgQH4FTGeU3hy5k3kL7Op59XafLlEyaNAmPPvoorly5AoPBIPtiR1V5eLAvICJSFfst9Vi7o28t2NAIrgVJ5udwlivVBV2Z+qJm9smTJdzVOrOthXqlgZU778V8Tc7Sfy232SKdG+VI8NrdTqZrXI/66NKgKr4a11m23TwAsjUnKz2nAKIo4kLmLQCWRTOsPf+WQnaLnKPTarBsTCdMTm1sKh7jjcWJnQ6yrl+/jsmTJyMhwfo6BVQxWbuDRUTkz9hvqcdaIGKtSzBmstzhSobH0eGC0uDQ1zNfbCwd5RJBVhFYnZPb+oykQZbSnCxHSYMoU3EN2Tb757AVDEr9b+od+Hx0R3SxE2Q1qxGNFWM7o3P9qvK2mjXGVsa2SC/iwMVs02Pz8u+A8uLVQ9rWBAC0TY612UayrWZsGP6Z2si0Dlp6TqGdZ7jP6SDrvvvuw6ZNmzzRFgogzGQRUaBgv6UepYtAwHp2QVAjyHJpuKD6Q6xCgzToXD9Otk3NrtCTmSy1+mxb2T5pYOVIcQprlNbJkp7NkYDR0eGpyXHh6Nk43qn2SZkvjGzrdQ0GEWclhVWqRYZYHKP0/Me71sWSx2/H56M7utxOKhdf9rln5Ho+yHJ6Tlbjxo0xffp0/PHHH2jVqpXFBOKnn35atcaRf5H+cWXhCyIKFOy31GOtnLT1TJb7F/iuDRd0IS9lZ7zgve1qYc59rdDoxZ9Q7Mr57VB7hIh8uKA657YWZAOAVjIxT2ttkp4DlIYGSq85HPmYnFknzR0WJdxtvK5BFE1BVPMa0Yrl2hvER1ps02k1uKNJdTdbSkbVo0uLXaR7YbigS9UFIyMjsXnzZmzevFm2TxAEdlZERORX2G+px9o1trUbb64OF2xfp4rpe1ee7+hFtr0L9s7147DjdKbVY+9oWh1vrz+OqFCdaZFZV6k9QkQ+XFDdcyuRDRd0481Ig03jz14aszlykzelQVV8vv0cQoNcD/ZcYT6navx/9pi+14uiaY2sutXCZcd9MaYjdp3JxEO3J3u+kZVctYhgAMD1vCKPv5bTQdaZM2c80Q4KANIJzxpB8Pn4dSIiR7DfUo+1TIa1y16N4HxWo0OdKlj4yG3l53ZluKArhS8UtmnszAVqWTMGv03pgerRoWj9yq9Ov6aUGuXupaSnU626oI2OP0grzWSpNSer9F9pwOjIqfu2SMRnozqgeQ3HFoJWS4lexKmMXFy7WYj2darg50Nppn0XMvPx8trDACznY3VvFI/ujVwftkiOM65X5o31x5wOsqSMF90sglD58EdORIGI/ZZ7rF2X2J6T5dzFzPheDWTr17hSnbBIpeFi0mDB2pC7htXtlyV3hCeHC6o1xN9WmXvp8Dd3MllK62Q5+14EQcCdTb1T6GZwmySsPXAZQOnvXe+3S7Pl341PkR0nHWJqXIiYvM/462OtUqqaXMqjLlu2DK1atUJYWBjCwsLQunVrfPHFF2q3jfyM9NeRc7KIKJCo1W9t2bIFgwYNQlJSEgRBwJo1ayyOOXLkCAYPHoyYmBhERETg9ttvx/nz5037CwoKMGHCBFStWhWRkZEYOnQorl696s7b8xprd3+tdQmCCyXczSu2uXK9Xlzi2AXUsu3nTN8rXXPJgiyFrIqatKqPbBMUvnOPrevSYMkbcGfNL9k6WUpzslw+s2e8+1BbPNGtHgDI1tLadeaG1edEhthfu4s8w/j7443RWE7/l37nnXcwfvx4DBgwAN988w2++eYb9OvXD0899RTeffddT7SR/JC//ZEjIrJGzX4rLy8Pbdq0waJFixT3nzp1Ct26dUPTpk3x+++/46+//sKMGTMQGlqemZk8eTJ++OEHfPvtt9i8eTMuX76M++67z6336C1W18myMSeryMkiEeY38Vy5YHdpuKDCe9PKhq55tufr0sB2GXFnyRfwVavwhWOvF+RGxCg9j1Yhk+VvWWitRkByXLjF9qIS67+D8dGhVveRZxl/f7yQyHJ+uOCCBQvwwQcfYMSIEaZtgwcPRosWLfDKK69g8uTJqjaQ/JOf/Y0jIrJKzX6rf//+6N+/v9X9L774IgYMGIA333zTtK1Bgwam77Ozs/Hpp59ixYoVuPPOOwEAS5YsQbNmzbBjxw507tzZ4pwAUFhYiMLC8pLDOTk5DrdZTdYusm3NybrmZKlk88yXK8GNrQtcaxTnZHlhvZI/nr8Df1/OwV3N1R3eJi97rs45bQ0XlF60hihUznOUNHuoVF3QH0fSKM1Bu5FvvbBCtMJCxOQdxl8fW5Uy1eL0/4IrV66gS5cuFtu7dOmCK1euqNIo8n/+dieJiMgab/VbBoMB//3vf9G4cWP07dsX1atXR6dOnWRDCvfs2YPi4mKkpqaatjVt2hS1a9fG9u3brZ57zpw5iImJMX0lJ/umCpnVwhdWS7g731eY18lwJSmi2pwspdLhKnd/taqEo0+LRI/2q2qdOcVsMV4paaAR7EYmS7HwhQfW/FJTkMLiy0u3nbV6fHgwgyxfMQ0X9EImy+n/BQ0bNsQ333xjsf3rr79Go0aNVGkU+Sdv/EISEanNW/1Weno6cnNz8cYbb6Bfv3749ddfce+99+K+++4zlY5PS0tDcHAwYmNjZc9NSEhAWlqawllLTZ8+HdnZ2aavCxcuqNZuZxispLKsF75w/jXMz+W16oJ25mT5YwbFFunnplZGrl3tKlj9jy7Y9WJvi32NqkeiZ+N4DL2tlluvJ/2YjZXgZNUF/TDK0jlZQTMsiHOyfMWbSQKnQ+lZs2bhoYcewpYtW9C1a1cAwNatW7FhwwbFToyIiMiXvNVvGQylF/b33HOPaQhi27ZtsW3bNixevBg9e/Z0+dwhISEICQlRpZ3usDZc0FpBDFcCE/OhV66cQ63qzPIFfQOLYOV7d7WrXUVxu0Yj4PPRHd0+v6zwhXGdLA+Uo1eTTiGTZUtYsHfX76Jy5ZksPxwuOHToUOzatQvVqlXDmjVrsGbNGlSrVg27du3Cvffe64k2kp+wNRabiMhfeavfqlatGnQ6HZo3by7b3qxZM1N1wcTERBQVFSErK0t2zNWrV5GYmKhaWzzF2nDBv68ozxFzabig2XNcKeHuCqV3plRdMFB4ooS7NyhWF3SglL4vOZ/J4nBBXzGVcPfCazn1Uy4uLsaTTz6JGTNm4D//+Y+n2kRERKQKb/ZbwcHBuP3223Hs2DHZ9uPHj6NOnToAgPbt2yMoKAgbNmzA0KFDAQDHjh3D+fPnkZKSYnFOf+PsZHFXFqW1zGQ5fQqX+Lq6oNqkwUggNV0payVY2e8vnM9kcbigrxj/X/jdnKygoCB89913nmoL+TvzX0gmtojIz6ndb+Xm5mL//v3Yv38/AODMmTPYv3+/KVP13HPP4euvv8bHH3+MkydPYuHChfjhhx/wj3/8AwAQExODMWPGYMqUKdi0aRP27NmDxx9/HCkpKVYrC/oTg5NTnSJcWA/I/CLaW3NwlC66ZK/thxf3tsgDq8BpvKAQ2Cpt8ydKhS9sCWeQ5TPlmSw/HC44ZMgQxcUXPS0zMxPDhw9HdHQ0YmNjMWbMGOTm5jr0XFEU0b9/f6sLR5JjGFMRUSBSs9/avXs32rVrh3bt2gEApkyZgnbt2mHmzJkAgHvvvReLFy/Gm2++iVatWuGTTz7Bd999h27dupnO8e677+Luu+/G0KFD0aNHDyQmJmLVqlWqtM/TnM1kuVJFzWKdLC9dVCu9N1l1wQAKVMz5Y/bHGmlbjaPw/H1OltbZ4YIMsnymvIS751/L6b9+jRo1wuzZs7F161a0b98eERERsv1PP/20ao2TGj58OK5cuYL169ejuLgYjz/+OMaNG4cVK1bYfe68efNYclwFseFB8g38SIkoAKjZb/Xq1cvuhOnRo0dj9OjRVveHhoZi0aJFVhc09mfOBlmuZLLMhwu6UQ3cKfbWyQqkQAUwX8DXd+1wllZh/pW/r5MVJGlzbHgQsvKLrR7bu2l1RIVwTpaveHO4oNM/5U8//RSxsbHYs2cP9uzZI9snCIJHgqwjR45g3bp1+PPPP9GhQwcApYtLDhgwAG+99RaSkpKsPnf//v14++23sXv3btSoUcPua/nLgo/+qEVSDJ7r2wQ1Y8NKNzC1RUQBwBf9VkXl7N3fCAczWUFaAcX60pObB1neukmqXMJd2g6vNEM1/j7EzppAXCcrVJKZiosItgiyQnQaFJYtkP3pqNu92jaSK/9d8vxFrNNB1pkzZzzRDpu2b9+O2NhYU4AFAKmpqdBoNNi5c6fV6lD5+fl45JFHsGjRIoerNs2ZMwezZs1Spd0V0YQ7Gvq6CURETvFFv1VROZ/JcuwyQ6fRoFivB6AwJ8uHwwU1TgwX/GxUB7y4+hDeebCt2k1ziadKuHuaPANnmcnyx5FJ0sxU1YhgnM7Ik+1/JrUx5q47ioc71vZ208iMaU6WvxW+AIDZs2cjPz/fYvutW7cwe/ZsVRplLi0tDdWrV5dt0+l0iIuLs7l44+TJk9GlSxfcc889Dr+Wvyz4SERE6vBFv1VRORtkRYfqMOz2ZLvHSauz+aqEu9JaX0pZFWvubJqA7dN7I6VBVbWb5jZ/DEwcofSZ+2MmKzK0PMiqHhUq2/dc3yZ4skd9/DipG2bf08LbTSMzpuGCXngtp4OsWbNmKRacyM/PdzoDNG3aNAiCYPPr6NGjzjYRALB27Vps3LgR8+bNc+p5ISEhiI6Oln0REVHgUrPfquycrS4YHRaErg2r2T0uWDIuz3K4oHOvqaRz/Ti7x5QoBFlaf6+4YEOgzskKkhSRqBFTOj3B3zNZkZJMlnT++pM96mN8zwbQaAS0rBmDIG9NMCSryjNZfjhcUBRFxV/wAwcOIC7O/h8xqWeffRajRo2yeUz9+vWRmJiI9PR02faSkhJkZmZaHQa4ceNGnDp1CrGxsbLtQ4cORffu3fH777871VYiIgpMavZblZ3zmawgh4b72cxkqZC6uK12Few4nWnzGMV1smRFGAJLwK6TpRFwYGYf6EXRVIVPWrzPH+eXSeceSjNZt9Wp4rUlCMgxxp+GXy1GXKVKFVN2qXHjxrIOS6/XIzc3F0899ZRTLx4fH4/4+Hi7x6WkpCArKwt79uxB+/btAZQGUQaDAZ06dVJ8zrRp0/DEE0/ItrVq1QrvvvsuBg0a5FQ7iYgo8Hii36rsnC7hHqJ1aHiXTnIVbX5RqsZFtSOB2oqd5y22+XtVO1vkxSICq+0xZtWMpQGjP8YsGo2Au1vXwMFL2RjcNgnv/nYcgDxDS/7B2A/4VXXBefPmQRRFjB49GrNmzUJMTIxpX3BwMOrWreux1eqbNWuGfv36YezYsVi8eDGKi4sxceJEDBs2zFRZ8NKlS+jduzeWLVuGjh07IjExUTHLVbt2bdSrV88j7SQiIv/hy36ronK2umBYkNZUNdAW6WKu5nOw1LhOdSTIup5XpPC88u8DLE4J2MIXSjQBEDAufOQ2AEB+UYmPW0K2hAZp0LJmtGyIp6c4/AojR44EANSrVw9du3aFTufdGv/Lly/HxIkT0bt3b2g0GgwdOhTz58837S8uLsaxY8cUJzcTEVHl4+t+q6LJLypBsd65SVkhOi30BvsXndK5KubruqoxB0fnYvpDvhhxgAmAwMRRQgBFjKG68nLuIte68Tu1qoTjx0ndvfJaTvc4UVFROHLkCFq1agUA+P7777FkyRI0b94cr7zyCoKDg1VvJADExcXZXHi4bt26diexeWOSGxER+Rdf9VsVSW5hCVq98ovTQ2xCgjQoKHZkTpYkyDILCNS4pnZ1XoxsrSl/HKdmg6zkfGA13UIgrfml0QhoXiMal7JuoXWtWF83h3zI6ST8k08+iePHS8eanj59Gg899BDCw8Px7bffYurUqao3kIiIyB3st9y3/3yWS3MYtBrBoWF20mF55sMF1chkuVoGXo2iG74iVJwYS9b+QPiRrJ7QBVum3oFqkSG+bgr5kNNB1vHjx9G2bVsAwLfffouePXtixYoVWLp0Kb777ju120d+jGlwIgoE7LfcFxrk/MSopolRaFMr1qHMQ7ikOpt5xsjes78Y0xGNqkfaPMbVYElWXVBwrD3+QjbCzs+zP/YEWgGSEJ0WMWFB9g+kCs3pv5qiKMJQtlDGb7/9hgEDBgAAkpOTce3aNXVbR0RE5Cb2W+5zJUj55qkUhzJZrWvFYOIdDa2+lr3nd28Uj/VTeto8xtUgQ9oW48V9oNxeFAJ5PpmZQF3ziyo3p4OsDh064LXXXsMXX3yBzZs3Y+DAgQCAM2fOICEhQfUGEhERuYP9lm+EB5WtcWTjqvj1e1ti7cRusrv+FsMFVQgRXD1DQBe+kAj0wMTfFyMmUuJ04Yt58+Zh+PDhWLNmDV588UU0bFh692nlypXo0qWL6g0kIiJyB/st9zlzYbvqH10QrNWYilnYCrKCykoJlkhqwwfr5Pd/1bimdjX7pKkowwUDptXK5Gt++a4dRM5wOshq3bo1Dh48aLH93//+N7RarcIziIiIfIf9lnfdVruK7LGti2LjxbN0kWN/KjahlV3c+0+7HBFgzbUpkKoLEhmpthR1aGgogoI4yY+IiAID+y0vsXFNbLxgTooNs36MCkGXdAmXNsmxDj/PnwI+Z0mzV4Eel0h/DAH+VqgScTqTVaVKFcVhA4IgIDQ0FA0bNsSoUaPw+OOPq9JAIiIid7Df8i1b5dONCw/XjA3DF2M6IjbMcs0ytS+qvx7XGU1nrHPoWCGQ5wIFWHNtkQeMFeiNUYXmdJA1c+ZMvP766+jfvz86duwIANi1axfWrVuHCRMm4MyZMxg/fjxKSkowduxY1RtMRETkDPZbjsu4WYgvdpzDQ7cno6Yku6Q3uF5Tz1YmSqspH1DTvVG84jFqX1M7cz55dUF12+FpFSkW0QjK3xP5M6eDrD/++AOvvfYannrqKdn2Dz/8EL/++iu+++47tG7dGvPnz6/0nRUREfke+y3HTfpyL3aczsQPBy5j0796mbaLDq5E3LeFZbVGWxfFrWrG2D2n2kUbnFmYOJCHqQVae22SlXCvUO+MKjCn52T98ssvSE1Ntdjeu3dv/PLLLwCAAQMG4PTp0+63joiIyE3stxy343QmAODMtTzZdkcSWfe1q4nFj7a32K5UqODXyT3w5djOqFctwu55VakuKGm/M4UTlIapORhv+lxFCkbkixH7sCFETnA6yIqLi8MPP/xgsf2HH35AXFwcACAvLw9RUVHut46IiMhN7LccZ+263JHhghqNoHhhrxTUNE6IQkqDqo61yaGjbBMlRdydKqShMEzNECBRlqyEe4AHXIHdeqqsnB4uOGPGDIwfPx6bNm0yjW3/888/8dNPP2Hx4sUAgPXr16NnT9urrxMREXkD+y3H6TQCivWWQYQjwwWtXQi7XXJb5UyWyy9d9j4CJsgK4KGO5li2nQKR00HW2LFj0bx5cyxcuBCrVq0CADRp0gSbN282Ler47LPPqttK8ksB0s8QUSXHfstxpcPjLP+4OzJc0NqFsMbNxWJ8uZCuoDBMzY0aID4T6DEKgywKRE4HWQDQtWtXdO3aVe22EBEReQT7Lfd8tvWM3WOsXQe7e4Fs/vTRXeth19nrOHQpx63zOvTasu8D60I/0NprUwV6K1R5uBRkGQwGnDx5Eunp6TAYDLJ9PXr0UKVh5P94Y4mIAgX7LceIClksANh4NN3ucz0WZJk9fq5vE4xe+qdT53A1+SQbclf2faPqkTiRnouIYK2LZ/WOitRHs9gFBSKng6wdO3bgkUcewblz5yzGaAuCAL1er1rjyL9xuCARBQL2W7Z98Psp3CrWY3JqIzfPpHwl7PaULLMhe2EqBjdtk2Ox/0KWjdcu/954of/pyNvx3oYTeLJnfdXa4WmBHnAFeuEOqpycDrKeeuopdOjQAf/9739Ro0YN/uITEZFfY79lXUGxHnPXHQUADL2tplvn8lS2QXra8b0aALCecbPG2k3BetUicPBSttXqibIS7mXf164ajrcfbOPU6/tCRfo1ZyaLApHTQdaJEyewcuVKNGzY0BPtIR+LCQtC9q1ihAa5OVOZiMhPsN+yrkQSXBQUGyz2FxTr8dp//3boXJ66qJdXyVNnrarmNaLx95Uc9G5WHT8cuOzQawfavCClADFQBXr7qXJy+kq6U6dOOHnypCfaQn7gq3GdcWfT6vhufBdfN4WISBXst6wzHz5pHrws2XoW/9lx3qFzWbsQdvfy2NHMY9vkWKv7zDNfix9tj/eH34YBLWs4HBwGWoW7AGuuTRXpvVDl4XQma9KkSXj22WeRlpaGVq1aISgoSLa/devWqjWOvK9ZjWh8Nup2XzeDiEg17Lesk4Yef57NtNi/88x1h8/ljUyWkVIia+njt2PDkXScvZ6HBRvlQbV58Fi7ajhqVw0vPb+VsvWlry3NBgUW+WLEPmuGKqTt53xwChROB1lDhw4FAIwePdq0TRAEiKLICcREROR32G9ZJ71gfWnNIYv9vx/LcPhcnsr0KJ5V4UI7NjwYQ9vXwsKNJ1R4ActdgRaoVKS5h4GWRSQCXAiyzpyxv1YGERGRv2C/ZUMAZAVk2SQHrrX1llPLUKcsa6V4fivblz5+O3ILS0yPA+1CP4Cnk1kItM+eCHAhyKpTp47idoPBgJ9++snqfqp4AqBvJiJiv+UlHhsuqLDNVnXBEoNllJXaLAEjUuqge6N4h14zPFiLXk2q479/XSlvRyBf5wd04wM/SKTKyaXFiKVOnjyJzz77DEuXLkVGRgaKi4vVaBcREZFHsN8q52wpdFusFr5we50s6WuUsjUvp1hvuTM0SIvZ97S0e35zgVw6PMDjKpmK9F6o8nCpTvetW7ewbNky9OjRA02aNMG2bdswc+ZMXLx4Ue32kR/j3zwiChTst5SpWUTAWkBSIybMrfMqBW96Gw1/uGOy2+c37ZMtRhxYvV4gF+0wV5Hml1Hl4VSQ9eeff+LJJ59EYmIi5s2bh3vuuQeCIOD999/HU089hYSEBE+1k/wQhwsSkb9jv2Wbmn/HrV0HR4TosGN6b3XOW/bAYGXxYACoUzUCh2b1xbge9Z0/v+VeB48jIpJzeLhg69atkZOTg0ceeQTbtm1DixYtAADTpk3zWOOIiIhcxX7LPvN1stxhK9uQGBOq2usA8kWUlUSG6BzO3tg6Tmkh5EDEAJHI+xzOZB07dgw9evTAHXfcgebNm3uyTURERG5jv2WfuyFWy5rRpu89dR2vNCdLbyfIUu21Jd8H8vwsIvI+h4Os06dPo0mTJhg/fjxq1aqFf/3rX9i3bx/HyRIRkV9iv2WfO4msUV3q4sUB5cGrpz5XpQxSg+qR6p3fRrudLR9PRGTkcJBVs2ZNvPjiizh58iS++OILpKWloWvXrigpKcHSpUtx/PhxT7aTiIjIKey37HO1uuDDHWvjlcEtEBasNW3zWAl3wfL72YNb4OGOtfH9hK5Wn2dwMIK0OVzQ4SP9W+C2nChwuVRd8M4778R//vMfXLlyBQsXLsTGjRvRtGlTtG7dWu32ERERuY39lhUuZrKy8osAyIfQeWy4oMK2qpEhmHNfK7RJjrX6PIdHFNpouLy6oIPnIyKCE0FWfn6+xbaYmBj84x//wO7du7F371706tVLzbYRERG5jP2We/48m2l138+H0gDIh/J5qsS5RlaK3PHXcHQopMOFLwJ4vGAgt50oUDkcZFWrVg133303PvroI6SlpVnsb9u2LebPn69q44iIiFzFfss+W3HIA4u3232+0lA+tbl6XkeHC9p8bWkJd7fP5juB3HaiQOVwkHX06FH07dsX33zzDerWrYtOnTrh9ddfx8GDBz3ZPiIiIpew37LP3ThEqfKf2lzNwjhant7m+aXDBV2aYOEfmMgi8j6H/2TUrl0bkyZNwm+//YarV6/imWeewcGDB9G9e3fUr18fzzzzDDZu3Ai9Xu/J9hIRETmE/ZZ9rha+SCpb90rjjVSWhDMv4fCULMdirIBeJ6sikRZbIfJnLt2XiYmJwcMPP4yvvvoKGRkZ+PDDD6HX6/H4448jPj4ey5cvV7udRERELlOr39qyZQsGDRqEpKQkCIKANWvWWD32qaeegiAImDdvnmx7ZmYmhg8fjujoaMTGxmLMmDHIzc114925ztVM1sxBpaXb/bkwxNju9RGs0+CxznVsHmd7TpYXUnXkkKn9mmBI2ySk1K/q66YQOUTn7gmCgoJw11134a677sKCBQuwb98+lJSUqNE2IiIi1bnTb+Xl5aFNmzYYPXo07rvvPqvHrV69Gjt27EBSUpLFvuHDh+PKlStYv349iouL8fjjj2PcuHFYsWKFy+/JVa6OFgwPLr18cLUohauceYXkuHAcntUXQVrb95NtrpMl+d5ThT28oSJk4f7Rq6Gvm0DkFKeDLIPBAI3CwGRRFHHhwgW0a9dOlYaR/3N0vDsRkS+p2W/1798f/fv3t3nMpUuXMGnSJPzyyy8YOHCgbN+RI0ewbt06/Pnnn+jQoQMAYMGCBRgwYADeeustxaDMk1z9O24MOGTD6excx88d2grPf3cQs+9p4dJrusJegAU4UV3Q/eYQUSXi8HDBnJwcPPjgg4iIiEBCQgJmzpwpG8eenp6OevXqeaSRREREzvJFv2UwGPDYY4/hueeeQ4sWlsHE9u3bERsbawqwACA1NRUajQY7d+60et7CwkLk5OTIvtTg6r0y49BAaRbI3nDBh26vjUOz+mJESl3XXhTeL+Agqy4YyFFWILedKEA5HGTNmDEDBw4cwBdffIHXX38dy5Ytwz333IOioiLTMcxsVC5cd4OI/Jkv+q25c+dCp9Ph6aefVtyflpaG6tWry7bpdDrExcUplpk3mjNnDmJiYkxfycnJqrbbWZqyiEraDTiSNYoMcXuWguqUujJBYV8gDxckIu9zOMhas2YNPvzwQ9x///144oknsHv3bmRkZGDQoEEoLCwE4NmLblcnCm/fvh133nknIiIiEB0djR49euDWrVsea2dlwqCaiPyZt/utPXv24L333sPSpUtV7w+nT5+O7Oxs09eFCxdUOa/rmSzL4YJ/nLzmfoPs8Mx1hmNzsgI5xNIyQCTyOoeDrIyMDNSpU16hp1q1avjtt99w8+ZNDBgwAPn5+R5poNHw4cNx+PBhrF+/Hj/++CO2bNmCcePG2XzO9u3b0a9fP/Tp0we7du3Cn3/+iYkTJyqOzScioorF2/3W//73P6Snp6N27drQ6XTQ6XQ4d+4cnn32WdStWxcAkJiYiPT0dNnzSkpKkJmZicTERKvnDgkJQXR0tOzLl4xDA6XZnf+d8HyQ5XUVJMpytVQ/EbnO4bx97dq1ceTIEdn49aioKPz666/o06cP7r33Xo80EHB9ovDkyZPx9NNPY9q0aaZtTZo0sflahYWFpjucAFQb905ERN7l7X7rscceQ2pqqmxb37598dhjj+Hxxx8HAKSkpCArKwt79uxB+/btAQAbN26EwWBAp06dVG2PI1y9+DZmlKQJku6NqqnRJB+w/hloZHPOAjfK2nE609dNIKp0HE7p9OnTB0uWLLHYHhkZiV9++QWhoaGqNkzKlYnC6enp2LlzJ6pXr44uXbogISEBPXv2xB9//GHztfxt3DsREbnGE/1Wbm4u9u/fj/379wMAzpw5g/379+P8+fOoWrUqWrZsKfsKCgpCYmKi6QZfs2bN0K9fP4wdOxa7du3C1q1bMXHiRAwbNszrlQUB9wtfSAOP0V0rXvErnaSaR+CGWETkCw5nsmbNmoXLly8r7ouKisL69euxd+9e1Rom5cpE4dOnTwMAXnnlFbz11lto27Ytli1bht69e+PQoUNo1KiR4vOmT5+OKVOmmB7n5OQw0CIiCkCe6Ld2796NO+64w/TY2F+MHDkSS5cudegcy5cvx8SJE9G7d29oNBoMHToU8+fPd6odanF1EJlSVidYV/GG4mulQVYAZ7KIyPscDrKqVKmCKlWqWN0fFRWFnj17OvXi06ZNw9y5c20ec+TIEafOaWQwGAAATz75pGmYRrt27bBhwwZ89tlnmDNnjuLzQkJCEBIS4tJrEhGR//BEv9WrVy+niv6cPXvWYltcXJxPFh5W4u46WRpNxRhOZ41W9v582BAiCjhO11ItKChQbWjgs88+i1GjRtk8pn79+i5NFK5RowYAoHnz5rLtzZo1w/nz511vNBERBRQ1+62KxuVMVlnSShp36LSej0K8FccZPxd5Jss7r01EFYNTQdaNGzdw9913Y+vWraq8eHx8POLj4+0e58pE4bp16yIpKQnHjh2TbT9+/Dj69+/vfuOJtYqIyO+p3W9VNO6WcK8IhSGUPgNjyXOtLH0VmO8PAJ5JVZ4iQUSe4/AA6itXrqBHjx5o06aNJ9ujyJGJwpcuXULTpk2xa9cuAKVjp5977jnMnz8fK1euxMmTJzFjxgwcPXoUY8aM8fp7ICIi7/JlvxU43BsuKI2rtF4YTyd4KdAxDoPUVZDhgtGhQb5uAlGl41Am68SJE+jTpw969OiB999/39NtUmRvonBxcTGOHTsmW/fkmWeeQUFBASZPnozMzEy0adMG69evR4MGDXzxFiocAcxmEZF/8od+KxC4W11QGmTpvBFkeSnQMQaMWsm6moFc+MIbATARyTkUZHXv3h3du3dXLIXrLfYmCtetW1dxAu+0adNk62SRehhgEZG/8od+qyIzrZOFCjBcUGGbMSbRSt5TYL67UgyyiLzPoeGCeXl5qFmzJjSaileelYiIKh72W45xvYR76b/eHy7oHTWrhAMAtNrADyIBBllEvuBQJmv9+vUYOHAgoqKi8Oqrr3q6TURERG5hv+UYV4cLGi/a5YUv1GiR793RJB6zBrcEYJbJCuD3pw3kxhMFKIeCrM6dO2PLli3o27cvIiMj8fzzz3u6XURERC5jv+UY0d3CF5Jt3piz5ImXMJ9qsOTxjqbvK0oGqGO9OF83gajScXgcRYsWLfDHH3/gs88+82R7iIiIVMF+yz5XM1nGYKciZrKkvFHMw5P2z7wLG5/tibrVInzdFKJKx6l1surWrYs//vjDU20hIiJSFfst29xdJ0uayvLGnCVvlXA30gR4kBUbHozY8GBfN4OoUnJ6RrAjiwcTERH5C/Zb1rk7XFAag3hj2o9O692gJ9AzWUTkOyy7REREVEm5v06Wd6vv6bTevWypKHOyiMj7nBouCADXr1/HzJkzsWnTJqSnp8NgMMj2Z2ZmqtY4IiIid7HfUp9SkQtvZLJCdeoHWbbiTAZZROQqp4Osxx57DCdPnsSYMWOQkJAQ0CugExFRxcd+S33G4MMgSYV5MpM1rkd97Dh9HYPaJHnsNZSw9DkRucrpIOt///sf/vjjD7Rp08YT7SEiIlIV+y3r3B0uGBakNW2Li/BcgYUXBjTz2LltfQbSwhchHsiiEVHF5XSQ1bRpU9y6dcsTbSEiIlId+y3rXC18YcwGBmk12PVCbxhEIFQScFUkT9/ZEBm5RWhYPdLXTSGiAOJ0kPX+++9j2rRpmDlzJlq2bImgoCDZ/ujoaNUaR/7N1TugRETexH7LOnczWQBQPTpUncb4qSl9mvi6CUQUgJwOsmJjY5GTk4M777xTtl0URQiCAL1er1rjiIiI3MV+yzpX75V5o5Kgt4i8Y0hEHuB0kDV8+HAEBQVhxYoVnEBcyQkCs1lE5P/Yb1nnaoBRkYIsIiJPcDrIOnToEPbt24cmTZg+r+wYYBFRIGC/ZZ3LmSzWgCAissnpP5MdOnTAhQsXPNEWIiIi1bHfUh8zWUREtjmdyZo0aRL++c9/4rnnnkOrVq0sJhC3bt1atcYRERG5i/2Wda4XvmCQRURki9NB1kMPPQQAGD16tGmbIAicQExERH6J/ZYtrs7JUrkZREQVjNNB1pkzZzzRDiIiIo9gv6U+Fg8hIrLN6SCrTp06nmgHERGRR7Dfso4FjFwv/kFEZIvThS/mzJmDzz77zGL7Z599hrlz56rSKCIiIrWw37KOAQYRkWc4HWR9+OGHaNq0qcX2Fi1aYPHixao0ioiISC3st4iIyNucDrLS0tJQo0YNi+3x8fG4cuWKKo0iIiJSC/st6xwdLrjyqRQ0qxHt2cb4CGeXEZEnOB1kJScnY+vWrRbbt27diqSkJFUaRUREpBb2W9aJDkZZHerGIblKmIdbQ0RUcThd+GLs2LF45plnUFxcjDvvvBMAsGHDBkydOhXPPvus6g0kIiJyB/stdVTU+VuslEhEnuB0kPXcc8/h+vXr+Mc//oGioiIAQGhoKJ5//nlMnz5d9QYSERG5g/2Wdc4ETgxFiIgc53SQJQgC5s6dixkzZuDIkSMICwtDo0aNEBIS4on2ERERuYX9lnXOlHAPCdJ6riFERBWMw0FW7dq1MXjwYAwePBh33nknIiMjcfvtt3uybURERC5jv6WuEJ3T07iJiCoth/9ifvHFFwgJCcGECRNQrVo1PPTQQ1i+fDmysrI82DwiIiLXsN+yT3RiwGBoEIMsIiJHOfwXs2fPnnj77bdx4sQJbN26FW3btsWCBQuQmJiIO++8E/PmzcPp06c92VYiIiKHsd9ygBPDBUN1HC5IROQol25LtWjRAtOnT8eOHTtw5swZDBs2DBs2bEDLli3RsmVL/Pe//1W7nURERC5jv+U+nZaZLCIiRzld+MJcjRo1MG7cOIwbNw55eXn49ddfOZmYiIj8Fvutcs5UF2SMRUTkOKeDrL179yIoKAitWrUCAHz//fdYsmQJmjdvjldeeQX33nuv6o0kIiJyFfst65ypLhgXUTkDUSIiVzh9X+rJJ5/E8ePHAQCnT5/GsGHDEB4ejm+//RZTp05VvYFERETuYL+ljuGdamPobbWw+NHbfN0UIiK/53SQdfz4cbRt2xYA8O2336JHjx5YsWIFli5diu+++07t9hEREbmF/ZZ1zlUX1OLtB9ugX8saHmwREVHF4HSQJYoiDAYDAOC3337DgAEDAADJycm4du2auq0jIiJyE/st65wZLlhRCYKvW0BEFZHTQVaHDh3w2muv4YsvvsDmzZsxcOBAAMCZM2eQkJCgegOJiIjcwX6LiIi8zekga968edi7dy8mTpyIF198EQ0bNgQArFy5El26dFG9gURERO5gv2UdE1lERJ7hcHXB06dPo379+mjdujUOHjxosf/f//43tFouVEhERP6B/ZZ9IscLEhF5hMOZrNatW6Nly5Z44YUXsGvXLov9oaGhCAoKUrVxROQ7nKdAgY79FhER+YrDQda1a9cwZ84cpKenY/DgwahRowbGjh2LH374AQUFBZ5sIxERkdM80W9t2bIFgwYNQlJSEgRBwJo1a0z7iouL8fzzz6NVq1aIiIhAUlISRowYgcuXL8vOkZmZieHDhyM6OhqxsbEYM2YMcnNz3XmrLmMei4jIMxwOskJDQzFo0CB88sknuHLlCr777jtUrVoVzz//PKpVq4YhQ4bgs88+Q0ZGhkca6kqnlJaWhsceewyJiYmIiIjAbbfdVunL9RIRVRae6Lfy8vLQpk0bLFq0yGJffn4+9u7dixkzZmDv3r1YtWoVjh07hsGDB8uOGz58OA4fPoz169fjxx9/xJYtWzBu3Di3369LGGWBSXsi8gSnC18AgCAI6NKlC9544w38/fff2LdvH7p3746lS5eiVq1aip2Pu1zplEaMGIFjx45h7dq1OHjwIO677z48+OCD2Ldvn+rtIyIi/6VWv9W/f3+89tpruPfeey32xcTEYP369XjwwQfRpEkTdO7cGQsXLsSePXtw/vx5AMCRI0ewbt06fPLJJ+jUqRO6deuGBQsW4KuvvrLIePnK6/e29HUTiIgCnktBlrlGjRrh2WefxZYtW3D58mX06dNHjdOauNopbdu2DZMmTULHjh1Rv359vPTSS4iNjcWePXusPqewsBA5OTmyLyIiqlg83W8ZZWdnQxAExMbGAgC2b9+O2NhYdOjQwXRMamoqNBoNdu7cafU8nuqblBYjrls1QpVzExFVZg5XFzRau3at4nZBEBAaGopGjRqhUaNGbjdMyl6npHRHEQC6dOmCr7/+GgMHDkRsbCy++eYbFBQUoFevXlZfa86cOZg1a5aq7SciIt/xRb8FAAUFBXj++efx8MMPIzo6GkDpMPbq1avLjtPpdIiLi0NaWprVc3mqb1IqLsiiN0RE7nM6yBoyZAgEQbAo+2rcJggCunXrhjVr1qBKlSqqNNLVTumbb77BQw89hKpVq0Kn0yE8PByrV682rZGiZPr06ZgyZYrpcU5ODpKTk91/E0RE5BO+6LeKi4vx4IMPQhRFfPDBB26fz5t9k8BZSkREbnN6uOD69etx++23Y/369cjOzkZ2djbWr1+PTp06meZKXb9+Hf/617/snmvatGkQBMHm19GjR116YwAwY8YMZGVl4bfffsPu3bsxZcoUPPjgg4rrpRiFhIQgOjpa9kVUGfEyiyoKNfstRxgDrHPnzmH9+vWyfiQxMRHp6emy40tKSpCZmYnExESr5/RU38RMFhGRZzidyfrnP/+Jjz76CF26dDFt6927N0JDQzFu3DgcPnwY8+bNw+jRo+2e69lnn8WoUaNsHlO/fn2XOqVTp05h4cKFOHToEFq0aAEAaNOmDf73v/9h0aJFWLx4sd32ERFR4FOz37LHGGCdOHECmzZtQtWqVWX7U1JSkJWVhT179qB9+/YAgI0bN8JgMKBTp05uv76jDlzIQk5BsWJxwcoWYwmMKonIA5wOsk6dOqV4By06OhqnT58GUDqh+Nq1a3bPFR8fj/j4eLvHudIp5efnAwA0GnmyTqvVwmAw2H1NIiKqGNTst3Jzc3Hy5EnT4zNnzmD//v2Ii4tDjRo1cP/992Pv3r348ccfodfrTUPa4+LiEBwcjGbNmqFfv34YO3YsFi9ejOLiYkycOBHDhg1DUlKSSu/YvnsWbQUAzL6nhcU+Bh1ERO5zerhg+/bt8dxzz8nWFcnIyMDUqVNx++23AwBOnDih6lhxaae0a9cubN261aJTunTpEpo2bYpdu3YBAJo2bYqGDRviySefxK5du3Dq1Cm8/fbbWL9+PYYMGaJa24iIyL+p2W/t3r0b7dq1Q7t27QAAU6ZMQbt27TBz5kxcunQJa9euxcWLF9G2bVvUqFHD9LVt2zbTOZYvX46mTZuid+/eGDBgALp164aPPvpI5XftmIs3bvnkdYmIKjqnM1mffPIJhgwZglq1apk6pAsXLqB+/fr4/vvvAZTe6XvppZdUbejy5csxceJE9O7dGxqNBkOHDsX8+fNN+4uLi3Hs2DFTBisoKAg//fQTpk2bhkGDBiE3NxcNGzbE559/jgEDBqjaNiIi8l9q9lu9evWyKKAhZWufUVxcHFasWOFg6z2rRG/ZXiayiIjc53SQ1bRpU/z999/49ddfcfz4cQBAkyZNcNddd5mG5nkiU2SvU6pbt65F59aoUSN89913qreFiIgCh6/6rUCgVxg+X5ljrO6Nqvm6CURUQTgVZBUXFyMsLAz79+9Hv3790K9fP0+1i4iIyG3st2wrMTCTJfXxiA72DyIicoBTc7KCgoJQu3Zt6PV6T7WHiPwEJ79TRcB+yza9QpBVmXNZoUFaXzeBiCoIpwtfvPjii3jhhReQmZnpifYQERGpiv2WdcpBVuWSmVfk6yYQUQXk9JyshQsX4uTJk0hKSkKdOnUQEREh2793717VGkdEROQu9lty0vnLSkEWk9hERO5zOsiqrJODiYgoMLHfkpPWiFKck+XFthARVVROB1kvv/yyJ9pBRETkEey35KRhVbHesrpgZdOxbhx2neVQUiJSl9NzsgAgKysLn3zyCaZPn24a4753715cunRJ1cYRERGpgf1WOelwwR2nr1vsr2xFb4J0lev9EpF3OJ3J+uuvv5CamoqYmBicPXsWY8eORVxcHFatWoXz589j2bJlnmgnERGRS9hvyUkzWeHBOtzIL5btr2whh6aSBZVE5B1OZ7KmTJmCUaNG4cSJEwgNDTVtHzBgALZs2aJq44jId3jZQRUF+y056ZysIC3/p2s1/AyISH1OB1l//vknnnzySYvtNWvWRFpamiqNIiIiUgv7LTlRksvSi6wuqGOQRUQe4HSQFRISgpycHIvtx48fR3x8vCqNIiIiUgv7LTlpXGVQqHshVLI8NocLEpEnOB1kDR48GLNnz0ZxcekYbkEQcP78eTz//PMYOnSo6g0kIiJyB/stOWmQxcWIAR2HTBKRBzgdZL399tvIzc1F9erVcevWLfTs2RMNGzZEVFQUXn/9dU+0kYiIyGXst+Q4XFCOmSwi8gSnqwvGxMRg/fr12Lp1Kw4cOIDc3FzcdtttSE1N9UT7iMhHeN1BFQX7LTn5cEFmslj4gog8wekgy6hr167o2rWrmm0hIiLyGPZbpaRhlVImq7JhkEVEnuDQcMH58+ejoKDA4ZMuXrwYN2/edLlRRERE7mC/ZZ10MWKlOVmVLYtdt2qEr5tARBWQQ0HW5MmTnep8pk6dioyMDJcbRURE5A72W9bJMllKQVYlqy44rkd9jEipgy/GdPR1U4ioAnFouKAoiujduzd0OsdGF966dcutRhEREbmD/ZZ1sjlZHC6I0CAtZt/T0tfNIKIKxqHe5+WXX3bqpPfccw/i4uJcahAREZG72G9ZJx0uqLhOVuVKZBEReYRHgiwiIiJfYr9lnWydLJZwJyLyCKfXySKiyqGyzcsgqizszcky16h6pOcaQ0RUQblcwp2IiIgCj2hnHpb0BsvIlDqYfFdjTzeJiKjCYSaLiIioErGXu5IOF+zdLAGx4cEebQ8RUUXEIIuIiKgScaagIOdnERG5xukgy9bijleuXHGrMURERGpjvyUn2sllCbLvGWUREbnC6SDrtttuw/79+y22f/fdd2jdurUabSIiIlIN+y0zdjJZ0uwVM1lERK5xOsjq1asXOnfujLlz5wIA8vLyMGrUKDz22GN44YUXVG8gERGRO9hvyTlQUNCEMRYRkWucri74/vvvY+DAgXjiiSfw448/4sqVK4iMjMSuXbvQsiVXTCeqMHh1RRUE+y05e8MFZf/5+XeAiMglLpVw79+/P+677z588MEH0Ol0+OGHHyplR0VERIGB/VY5e4UvpEMENRwvSETkEqeHC546dQopKSn48ccf8csvv2Dq1KkYPHgwpk6diuLiYk+0kYiIyGXst+ScGC3IRBYRkYucDrLatm2LevXq4cCBA7jrrrvw2muvYdOmTVi1ahU6duzoiTYSERG5jP2WnP3FiCXfM5NFROQSp4Os999/H1999RViY2NN27p06YJ9+/bhtttuU7NtREREbmO/JWd/uKAg+d7DjSEiqqCcDrIee+wxxe1RUVH49NNP3W4QERGRmthvuY4xFhGRa5wufLFs2TKr+wRBsNqZERER+QL7LTm7mSzp94yyiIhc4nSQ9c9//lP2uLi4GPn5+QgODkZ4eHil66yIKipeW1FFwX5LzmBvTpbsPz//EhARucLp4YI3btyQfeXm5uLYsWPo1q0bvvzyS0+0kYiIyGXst+RshVjVIoNlj5nJIiJyjdNBlpJGjRrhjTfesLhbSERE5I8qc79lq7rgz//sAUGSvWKMRUTkGlWCLADQ6XS4fPmyWqcjIiLyqMrab9nKZMVHhciyVyzhTkTkGqfnZK1du1b2WBRFXLlyBQsXLkTXrl1VaxgREZEa2G/J2St8IcUQi4jINU4HWUOGDJE9FgQB8fHxuPPOO/H222+r1S4i8jHewKaKgv2WOcejLP4dICJyjdNBlsFg8EQ7iIiIPIL9lpz9xYgl3zOXRUTkEtXmZHna66+/ji5duiA8PByxsbEOPUcURcycORM1atRAWFgYUlNTceLECc82lIiIyI85MVqQmSwiIhc5lMmaMmWKwyd85513XG6MLUVFRXjggQeQkpKCTz/91KHnvPnmm5g/fz4+//xz1KtXDzNmzEDfvn3x999/IzQ01CPtJCIi3/OHfstf2V8ni5EVEZG7HAqy9u3b59DJPPmHedasWQCApUuXOnS8KIqYN28eXnrpJdxzzz0AgGXLliEhIQFr1qzBsGHDPNVUIiLyMX/ot/yV3eGC0u8r38dDRKQKh4KsTZs2ebodqjtz5gzS0tKQmppq2hYTE4NOnTph+/btVoOswsJCFBYWmh7n5OR4vK1ERKQuT/VbW7Zswb///W/s2bMHV65cwerVq2WFNURRxMsvv4yPP/4YWVlZ6Nq1Kz744AM0atTIdExmZiYmTZqEH374ARqNBkOHDsV7772HyMhIj7TZnHPVBRllERG5wuE5WadPn7a5gKG/SUtLAwAkJCTItickJJj2KZkzZw5iYmJMX8nJyR5tJxEReYYn+q28vDy0adMGixYtUtxvHKa+ePFi7Ny5ExEREejbty8KCgpMxwwfPhyHDx/G+vXr8eOPP2LLli0YN26cqu20RbQzK0u+TpaHG0NEVEE5HGQ1atQIGRkZpscPPfQQrl696taLT5s2DYIg2Pw6evSoW6/hrOnTpyM7O9v0deHCBa++PpG/4B1sCnSe6Lf69++P1157Dffee6/FPvNh6q1bt8ayZctw+fJlrFmzBgBw5MgRrFu3Dp988gk6deqEbt26YcGCBfjqq6+8tjCy/eGC5f/3GWQREbnG4SDL/G7gTz/9hLy8PLde/Nlnn8WRI0dsftWvX9+lcycmJgKARYd69epV0z4lISEhiI6Oln0RVUb27nYT+TtP9Fu22BumDgDbt29HbGwsOnToYDomNTUVGo0GO3futHruwsJC5OTkyL68gTdbiIhc4/Q6WWqKj49HfHy8R85dr149JCYmYsOGDWjbti2A0vlVO3fuxPjx4z3ymkREVHk5Mkw9LS0N1atXl+3X6XSIi4uzO5TdWADKXU6tk8UYi4jIJQ5nsozD98y3ecv58+exf/9+nD9/Hnq9Hvv378f+/fuRm5trOqZp06ZYvXq1qW3PPPMMXnvtNaxduxYHDx7EiBEjkJSUJJukTETKeAebAp2v+y01qTmU3e6cLCvfExGR4xzOZImiiFGjRiEkJAQAUFBQgKeeegoRERGy41atWqVuC8vMnDkTn3/+uelxu3btAJRWkOrVqxcA4NixY8jOzjYdM3XqVOTl5WHcuHHIyspCt27dsG7dOq6RRURUCXi735IOU69Ro4Zp+9WrV00jKhITE5Geni57XklJCTIzM+0OZTe+D3cZnKkuyCiLiMglDgdZI0eOlD1+9NFHVW+MLUuXLrW7Rpb5+HtBEDB79mzMnj3bgy0jIiJ/5O1+y5Fh6ikpKcjKysKePXvQvn17AMDGjRthMBjQqVMnj7bPyG7FRcHqAyIicpDDQdaSJUs82Q4iIiJVeaLfys3NxcmTJ02Pz5w5g/379yMuLg61a9c2DVNv1KgR6tWrhxkzZsiGqTdr1gz9+vXD2LFjsXjxYhQXF2PixIkYNmwYkpKSVG+vEnuJLFYXJCJyn08LXxCR/+LFFZGl3bt344477jA9njJlCoDSrNnSpUsdGqa+fPlyTJw4Eb179zYtRjx//nyvvQfnFiMmIiJXMMgiIkUBtPY4kdf06tXL5nA7R4apx8XFYcWKFZ5onoOcWYyYYRYRkSscri5IREREgc9ajPjl2M4W2xhiERG5hkEWESniDWyiikkpxhp6Wy2kNKgKwKyEO/8OEBG5hEEWERFRJaKUyeIQQSIidTHIIiIiqkQMClEWwyoiInUxyCIiIqpEnFkmS2D4RUTkEgZZRKSIl1ZEFZOoMCtLPlzQ9rFERGQfgywiIqLKRGlOlpXbKhrOzyIicgmDLCIiokrEXm5KGnBpNAyyiIhcwSCLiIioErFXXVCa1GKIRUTkGgZZRERElYi9OVnS3RwuSETkGgZZRERElYi96oLSIEzDqwQiIpfwzycREVElorROlnRgoN4gCbKYySIicgmDLCIiokpEMcSSxFKSGAtaBllERC5hkEVEigReXBFVTHaGCwbrNIrfExGR43S+bgARERF5j2LhC8n3MWFBeHVIS2gFAREhvEwgInIF/3oSERFVInZLuAN4rHMd7zSGiKiC4jgAIiKiSsRedUEiInIfgywiUsQZWUQVk3JtQf6PJyJSE4MsIlLEm91EFZOokMpinRsiInUxyCIiIqpEDLyDQkTkcQyyiEgRb2wTVVS2qwsSEZH7GGQRERFVIsrVBRlmERGpiUEWERFRJcLRgkREnscgi4iIqBJhCXciIs9jkEVEyjh6iKhCEpXmZPH/OxGRqhhkEZEy3u0mqpCYySIi8jwGWURERJWIQWmdLKauiYhUxSCLiJTxmouo0uBwQSIidTHIIiIiqkQ4XJCIyPMYZBEREVUiioUvfNAOIqKKjEEWERFRJaK8GLH320FEVJExyCIiIqpEOFyQiMjzGGQRERFVIkoxlsBUFhGRqhhkERERVSKiYgl3IiJSE4MsIlLEiy6iionDBYmIPI9BFhERUSWiVF2Qd1WIiNTFIIuIiKgSUawuyCiLiEhVDLLIZeHBWl83gYiInMTRgkREnhcwQdbrr7+OLl26IDw8HLGxsXaPLy4uxvPPP49WrVohIiICSUlJGDFiBC5fvuz5xlYSK8Z2RoukaHw5trOvm0JERA7iOllERJ4XMEFWUVERHnjgAYwfP96h4/Pz87F3717MmDEDe/fuxapVq3Ds2DEMHjzYwy2tPNomx+K/T3dHSoOqvm4KeQBLOhNVTEpzsvi/nYhIXTpfN8BRs2bNAgAsXbrUoeNjYmKwfv162baFCxeiY8eOOH/+PGrXrq12E4mIiPxen+aJaJoYjV8Op+GjLad93RwiogopYIIsNWRnZ0MQBJvDDQsLC1FYWGh6nJOT44WWEREReUd8VAjio0JwKj3XtE3DzDURkaoCZriguwoKCvD888/j4YcfRnR0tNXj5syZg5iYGNNXcnKyF1tJRETkJZK4ijEWEZG6fBpkTZs2DYIg2Pw6evSo269TXFyMBx98EKIo4oMPPrB57PTp05GdnW36unDhgtuvT0RE5G8u3rhl+t7AFYqJiFTl0+GCzz77LEaNGmXzmPr167v1GsYA69y5c9i4caPNLBYAhISEICQkxK3XJCIi8ndFJQbT9wbGWEREqvJpJis+Ph5Nmza1+RUcHOzy+Y0B1okTJ/Dbb7+halVWwSMiIs/R6/WYMWMG6tWrh7CwMDRo0ACvvvoqREmmSBRFzJw5EzVq1EBYWBhSU1Nx4sQJr7dVWmVQzyiLiEhVATMn6/z589i/fz/Onz8PvV6P/fv3Y//+/cjNLZ+427RpU6xevRpAaYB1//33Y/fu3Vi+fDn0ej3S0tKQlpaGoqIiX70NooDBORpEzps7dy4++OADLFy4EEeOHMHcuXPx5ptvYsGCBaZj3nzzTcyfPx+LFy/Gzp07ERERgb59+6KgoMC7jZXEVSV6BllERGoKmOqCM2fOxOeff2563K5dOwDApk2b0KtXLwDAsWPHkJ2dDQC4dOkS1q5dCwBo27at7FzS5xAREall27ZtuOeeezBw4EAAQN26dfHll19i165dAEqzWPPmzcNLL72Ee+65BwCwbNkyJCQkYM2aNRg2bJjX2iqdh8U5WURE6gqYTNbSpUshiqLFlzRYEkXRNMerbt26isebP4eIiEgtXbp0wYYNG3D8+HEAwIEDB/DHH3+gf//+AIAzZ84gLS0NqamppufExMSgU6dO2L59u9XzFhYWIicnR/blrmI9gywiIk8JmEwWERGRv5s2bRpycnLQtGlTaLVa6PV6vP766xg+fDgAIC0tDQCQkJAge15CQoJpn5I5c+Zg1qxZqra1UFL4gjEWEZG6AiaTRURE5O+++eYbLF++HCtWrMDevXvx+eef46233pINd3eFJ5YXyS8qMX0vLYJBRETuYyaLiIhIJc899xymTZtmmlvVqlUrnDt3DnPmzMHIkSORmJgIALh69Spq1Khhet7Vq1ct5g9LeWJ5kbHd6+P7/ZcBsIQ7EZHamMkiIiJSSX5+PjQaedeq1WphMJQOzatXrx4SExOxYcMG0/6cnBzs3LkTKSkpXm1ry5oxpu85XJCISF3MZBGRIlZwJ3LeoEGD8Prrr6N27dpo0aIF9u3bh3feeQejR48GAAiCgGeeeQavvfYaGjVqhHr16mHGjBlISkrCkCFDfNhyRllERGpikEVERKSSBQsWYMaMGfjHP/6B9PR0JCUl4cknn8TMmTNNx0ydOhV5eXkYN24csrKy0K1bN6xbtw6hoaE+a7fBYP8YIiJyHIMsIiIilURFRWHevHmYN2+e1WMEQcDs2bMxe/Zs7zXMDha+ICJSF+dkERERVXKck0VEpC4GWURERJUcYywiInUxyCIiIqrkDExlERGpikEWERFRZccYi4hIVQyyiEiRRmARd6LKgjEWEZG6GGQRkSLGWESVh8jhgkREqmKQRURWMMoiqiwYYhERqYtBFhEp0jDGIqo0DIyyiIhUxSCLiBRxuCBR5cHhgkRE6mKQRUSKBA4XJKo0GGIREamLQRYRKWImi6jyYCaLiEhdDLKISBFLuBNVHoyxiIjUxSCLiBQxxiKqPBhkERGpi0EWESlikEVUeRgYZRERqYpBFhEpYuELosqDIRYRkboYZBGRIq6TRVR5MJFFRKQuBllEpEjgeEGiSoRRFhGRmhhkEZFMvxaJAIBxPer7uCVE5Gkd6lQBAAy7vbaPW0JEVLHofN0AIvIvCx9ph7PX89EgPsLXTSEiD1sxtjMu3shH/fhIXzeFiKhCYZBFRDI6rQYNq/OCi6gyCNZpGGAREXkAhwsSERERERGpiEEWERERERGRihhkERERERERqYhBFhERERERkYoYZBEREREREamIQRYREREREZGKGGQRERERERGpiEEWERERERGRihhkERERERERqYhBFhERERERkYoYZBEREREREamIQRYREREREZGKGGQRERERERGpiEEWERERERGRinS+boC/E0URAJCTk+PjlhARVS7Gv7vGv8NUjn0TEZFvONo3Mciy4+bNmwCA5ORkH7eEiKhyunnzJmJiYnzdDL/CvomIyLfs9U2CyFuENhkMBly+fBlRUVEQBMHXzQFQGkEnJyfjwoULiI6O9nVzfI6fhyV+Jpb4mVjy989EFEXcvHkTSUlJ0Gg4ul3K3/omf/9d8gV+Jpb4mVjiZ2LJ3z8TR/smZrLs0Gg0qFWrlq+boSg6Otovf/l8hZ+HJX4mlviZWPLnz4QZLGX+2jf58++Sr/AzscTPxBI/E0v+/Jk40jfx1iAREREREZGKGGQRERERERGpiEFWAAoJCcHLL7+MkJAQXzfFL/DzsMTPxBI/E0v8TEgt/F2yxM/EEj8TS/xMLFWUz4SFL4iIiIiIiFTETBYREREREZGKGGQRERERERGpiEEWERERERGRihhkERERERERqYhBlg+88sorEARB9tW0aVPT/oKCAkyYMAFVq1ZFZGQkhg4diqtXr8rOcf78eQwcOBDh4eGoXr06nnvuOZSUlMiO+f3333HbbbchJCQEDRs2xNKlS73x9lx26dIlPProo6hatSrCwsLQqlUr7N6927RfFEXMnDkTNWrUQFhYGFJTU3HixAnZOTIzMzF8+HBER0cjNjYWY8aMQW5uruyYv/76C927d0doaCiSk5P/v727j4q6yv8A/h6ehyeRpxlAeVQBEQGXlsUHCGVDjseMVjMdOahRp5ZTUqxSx9ItFuVUx1pbQ0QCV0Q3to6Ry0NEYJAI6JFZQAQUxLZlYlNHQEke5vP7w8M3vzIa6ij4m8/rHM7xe++d+7339j3z7n6Z+YJ33333oczvbrm7u4+6TiQSCRISEgDo53UyPDyMt956Cx4eHpBKpfDy8kJKSgpufn6Pvl0nvb29SExMhJubG6RSKebOnYu6ujqhXt/Wg907zibtOJvEOJvEOJe042wCQOyh27p1K/n5+VFXV5fw87///U+of/HFF2nq1KlUVlZGJ06coN/97nc0d+5coX5oaIhmzZpFkZGRdOrUKSosLCR7e3t64403hDbt7e1kbm5Or732Gp0+fZo++ugjMjQ0pOLi4oc617G6dOkSubm50dq1a6mmpoba29uppKSEzp49K7RJS0ujSZMm0eHDh0mpVNKTTz5JHh4e1N/fL7RZvHgxBQQE0PHjx6myspKmTZtGq1atEuqvXLlCMpmMFAoFNTY20sGDB0kqlVJGRsZDne9YdHd3i66R0tJSAkDl5eVEpJ/XSWpqKtnZ2dGRI0eoo6OD8vPzydLSkv76178KbfTtOnnmmWdo5syZdPToUWpra6OtW7eStbU1/ec//yEi/VsPdu84m0bjbBqNs0mMc0k7ziYi3mSNg61bt1JAQIDWOrVaTcbGxpSfny+UNTc3EwCqrq4mIqLCwkIyMDAglUoltElPTydra2u6fv06ERFt2rSJ/Pz8RH2vXLmSoqKidDwb3UhOTqb58+fftl6j0ZBcLqf33ntPKFOr1WRqakoHDx4kIqLTp08TAKqrqxPaFBUVkUQioR9++IGIiD7++GOaPHmysE4j5/b29tb1lHRuw4YN5OXlRRqNRm+vkyVLltD69etFZU8//TQpFAoi0r/r5Nq1a2RoaEhHjhwRlc+ZM4c2b96sd+vB7g9n02icTb9O37OJc2k0zqYb+OOC46StrQ3Ozs7w9PSEQqHAhQsXAAAnT57E4OAgIiMjhbY+Pj5wdXVFdXU1AKC6uhr+/v6QyWRCm6ioKPT09KCpqUloc3MfI21G+phoCgoKEBwcjBUrVsDR0RFBQUHIzMwU6js6OqBSqURzmjRpEkJCQkTrYmNjg+DgYKFNZGQkDAwMUFNTI7QJCwuDiYmJ0CYqKgotLS24fPnyg57mPRsYGEBubi7Wr18PiUSit9fJ3LlzUVZWhtbWVgCAUqlEVVUVoqOjAejfdTI0NITh4WGYmZmJyqVSKaqqqvRuPdj942wS42y6M84mziVtOJtu4E3WOAgJCUFOTg6Ki4uRnp6Ojo4OLFiwAL29vVCpVDAxMYGNjY3oNTKZDCqVCgCgUqlEb04j9SN1d2rT09OD/v7+BzSze9fe3o709HRMnz4dJSUleOmll/DKK69g3759AH6Zl7Y53TxnR0dHUb2RkRFsbW3vau0mosOHD0OtVmPt2rUAoLfXyeuvv45nn30WPj4+MDY2RlBQEBITE6FQKADo33ViZWWF0NBQpKSk4L///S+Gh4eRm5uL6upqdHV16d16sPvD2TQaZ9OdcTZxLmnD2XSD0XgPQB+N3N0AgNmzZyMkJARubm749NNPIZVKx3Fk40ej0SA4OBjbtm0DAAQFBaGxsRG7d+9GXFzcOI9u/GVlZSE6OhrOzs7jPZRx9emnn+LAgQPIy8uDn58f6uvrkZiYCGdnZ729Tvbv34/169fDxcUFhoaGmDNnDlatWoWTJ0+O99DYI4azaTTOpjvjbOJcuh3OJv5N1oRgY2ODGTNm4OzZs5DL5RgYGIBarRa1+fHHHyGXywEAcrl81JN6Ro5/rY21tfWEDEsnJyfMnDlTVObr6yt8VGVkXtrmdPOcu7u7RfVDQ0O4dOnSXa3dRNPZ2Ymvv/4a8fHxQpm+XicbN24U7hr6+/sjNjYWr776KrZv3w5AP68TLy8vHD16FH19ffj+++9RW1uLwcFBeHp66uV6MN3hbOJsuhPOphs4l7TjbOJN1oTQ19eHc+fOwcnJCb/5zW9gbGyMsrIyob6lpQUXLlxAaGgoACA0NBQNDQ2ii6+0tBTW1tZCGISGhor6GGkz0sdEM2/ePLS0tIjKWltb4ebmBgDw8PCAXC4Xzamnpwc1NTWidVGr1aK7JN988w00Gg1CQkKENt9++y0GBweFNqWlpfD29sbkyZMf2PzuR3Z2NhwdHbFkyRKhTF+vk2vXrsHAQPy2ZWhoCI1GA0C/rxMLCws4OTnh8uXLKCkpwbJly/R6Pdj942zibLoTzqYbOJfuTK+zabyfvKGPkpKSqKKigjo6Oui7776jyMhIsre3p+7ubiK68fhTV1dX+uabb+jEiRMUGhpKoaGhwutHHn/6xBNPUH19PRUXF5ODg4PWx59u3LiRmpubadeuXRP28adERLW1tWRkZESpqanU1tZGBw4cIHNzc8rNzRXapKWlkY2NDX3xxRf073//m5YtW6b1cZ9BQUFUU1NDVVVVNH36dNHjPtVqNclkMoqNjaXGxkY6dOgQmZubT5jHfd5qeHiYXF1dKTk5eVSdPl4ncXFx5OLiIjwq9/PPPyd7e3vatGmT0EbfrpPi4mIqKiqi9vZ2+uqrryggIIBCQkJoYGCAiPRvPdi942wajbNJO86mX3AuacfZxI9wHxcrV64kJycnMjExIRcXF1q5cqXob2709/fTH//4R5o8eTKZm5tTTEwMdXV1ifo4f/48RUdHk1QqJXt7e0pKSqLBwUFRm/LycgoMDCQTExPy9PSk7OzshzG9e/bll1/SrFmzyNTUlHx8fGjPnj2ieo1GQ2+99RbJZDIyNTWlRYsWUUtLi6jNxYsXadWqVWRpaUnW1ta0bt066u3tFbVRKpU0f/58MjU1JRcXF0pLS3vgc7tXJSUlBGDUPIn08zrp6emhDRs2kKurK5mZmZGnpydt3rxZ9PhWfbtO/vGPf5CnpyeZmJiQXC6nhIQEUqvVQr2+rQe7d5xN2nE2jcbZ9AvOJe04m4gkRDf9SWrGGGOMMcYYY/eFv5PFGGOMMcYYYzrEmyzGGGOMMcYY0yHeZDHGGGOMMcaYDvEmizHGGGOMMcZ0iDdZjDHGGGOMMaZDvMlijDHGGGOMMR3iTRZjjDHGGGOM6RBvshhjjDHGGGNMh3iTxdhd+vOf/4zAwMDxHoZAIpHg8OHDd/Uad3d3SCQSSCQSqNXqBzKuR93I+tjY2Iz3UBhj7FdxNukHzqZHB2+y2IS0e/duWFlZYWhoSCjr6+uDsbExHn/8cVHbiooKSCQSnDt37iGP8uHSdYC+88476OrqwqRJk0bV+fj4wNTUFCqVSmfnG6vz589DIpGgvr7+oZ/7Zl1dXfjwww/HdQyMsYmFs2k0zqaHi7Pp0cGbLDYhRUREoK+vDydOnBDKKisrIZfLUVNTg59//lkoLy8vh6urK7y8vMZjqI8sKysryOVySCQSUXlVVRX6+/uxfPly7Nu3b5xG9+sGBgYeaP9yuVxryDPG9Bdn04PH2XRnnE2PDt5ksQnJ29sbTk5OqKioEMoqKiqwbNkyeHh44Pjx46LyiIgIAMD+/fsRHBwsvEmvXr0a3d3dAACNRoMpU6YgPT1ddK5Tp07BwMAAnZ2dAAC1Wo34+Hg4ODjA2toaCxcuhFKpvON49+7dC19fX5iZmcHHxwcff/yxUDdy9+vzzz9HREQEzM3NERAQgOrqalEfmZmZmDp1KszNzRETE4MdO3YIHwfIycnB22+/DaVSKXxUICcnR3jtTz/9hJiYGJibm2P69OkoKCgY20JrkZWVhdWrVyM2NhaffPLJqHp3d3ds27YN69evh5WVFVxdXbFnzx5Rm2PHjiEwMBBmZmYIDg7G4cOHRXcAL1++DIVCAQcHB0ilUkyfPh3Z2dkAAA8PDwBAUFAQJBKJcHd47dq1eOqpp5CamgpnZ2d4e3sDABoaGrBw4UJIpVLY2dnhhRdeQF9fnzCWkddt27YNMpkMNjY2eOeddzA0NISNGzfC1tYWU6ZMEc7PGGO3w9nE2cTZxMaMGJugVq9eTU888YRw/Nhjj1F+fj69+OKLtGXLFiIiunbtGpmamlJOTg4REWVlZVFhYSGdO3eOqqurKTQ0lKKjo4U+/vSnP9H8+fNF50lKShKVRUZG0tKlS6muro5aW1spKSmJ7Ozs6OLFi0REtHXrVgoICBDa5+bmkpOTE3322WfU3t5On332Gdna2gpj6ujoIADk4+NDR44coZaWFlq+fDm5ubnR4OAgERFVVVWRgYEBvffee9TS0kK7du0iW1tbmjRpkjDPpKQk8vPzo66uLurq6qJr164REREAmjJlCuXl5VFbWxu98sorZGlpKYxXGzc3N/rggw9Glff09JCFhQU1NjbS0NAQyWQy+vbbb0e91tbWlnbt2kVtbW20fft2MjAwoDNnzhAR0ZUrV8jW1pbWrFlDTU1NVFhYSDNmzCAAdOrUKSIiSkhIoMDAQKqrq6OOjg4qLS2lgoICIiKqra0lAPT1119TV1eXMI+4uDiytLSk2NhYamxspMbGRurr6yMnJyd6+umnqaGhgcrKysjDw4Pi4uKE8cbFxZGVlRUlJCTQmTNnKCsriwBQVFQUpaamUmtrK6WkpJCxsTF9//33orlmZ2cL/w0YY4yIs4mzibOJjQ1vstiElZmZSRYWFjQ4OEg9PT1kZGRE3d3dlJeXR2FhYUREVFZWRgCos7NTax91dXUEgHp7e4mI6NSpUySRSIT2w8PD5OLiQunp6UREVFlZSdbW1vTzzz+L+vHy8qKMjAwiGh1kXl5elJeXJ2qfkpJCoaGhRPRLkO3du1eob2pqIgDU3NxMREQrV66kJUuWiPpQKBSiN9FbzzsCAL355pvCcV9fHwGgoqIirWtCdPsg27NnDwUGBgrHGzZsEIXCyGvXrFkjHGs0GnJ0dBTWMD09nezs7Ki/v19ok5mZKQqypUuX0rp167SObWS9RtqOiIuLI5lMRtevXxeNd/LkydTX1yeU/etf/yIDAwNSqVTC69zc3Gh4eFho4+3tTQsWLBCOh4aGyMLCgg4ePCg6JwcZY+xWnE2cTTfjbGK3wx8XZBPW448/jqtXr6Kurg6VlZWYMWMGHBwcEB4eLnz2vaKiAp6ennB1dQUAnDx5EkuXLoWrqyusrKwQHh4OALhw4QIAIDAwEL6+vsjLywMAHD16FN3d3VixYgUAQKlUoq+vD3Z2drC0tBR+Ojo6tH55+erVqzh37hyee+45Ufu//OUvo9rPnj1b+LeTkxMACB8XaWlpwW9/+1tR+1uP7+Tmvi0sLGBtbS30fTc++eQTrFmzRjhes2YN8vPz0dvbe9vzSSQSyOVy0Vxmz54NMzOz287lpZdewqFDhxAYGIhNmzbh2LFjYxqfv78/TExMhOPm5mYEBATAwsJCKJs3bx40Gg1aWlqEMj8/PxgY/PJ2J5PJ4O/vLxwbGhrCzs7untaMMaZfOJs4m27F2cS0MRrvATB2O9OmTcOUKVNQXl6Oy5cvC6Hk7OyMqVOn4tixYygvL8fChQsB3AiVqKgoREVF4cCBA3BwcMCFCxcQFRUl+iKqQqFAXl4eXn/9deTl5WHx4sWws7MDcOMpUbd+3n6Etseljny+OjMzEyEhIaI6Q0ND0bGxsbHw75Ev9Go0mrtcFe1u7nuk/7vt+/Tp0zh+/Dhqa2uRnJwslA8PD+PQoUN4/vnndXa+6OhodHZ2orCwEKWlpVi0aBESEhLw/vvv3/F1NwfW3dA2Xl2sGWNM/3A2jR1n051xNv3/xr/JYhNaREQEKioqUFFRIXo8blhYGIqKilBbWyt8sfjMmTO4ePEi0tLSsGDBAvj4+Gi9+7N69Wo0Njbi5MmT+Oc//wmFQiHUzZkzByqVCkZGRpg2bZrox97eflRfMpkMzs7OaG9vH9V+5EuyY+Ht7Y26ujpR2a3HJiYmGB4eHnOfdysrKwthYWFQKpWor68Xfl577TVkZWWNuR9vb280NDTg+vXrQtmtcwEABwcHxMXFITc3Fx9++KHwBeWRu4Fjmauvry+USiWuXr0qlH333XcwMDAQvnzMGGO6xtn0C86m0TibGMCbLDbBRUREoKqqCvX19cLdQgAIDw9HRkYGBgYGhCBzdXWFiYkJPvroI7S3t6OgoAApKSmj+nR3d8fcuXPx3HPPYXh4GE8++aRQFxkZidDQUDz11FP46quvcP78eRw7dgybN28WPbL3Zm+//Ta2b9+OnTt3orW1FQ0NDcjOzsaOHTvGPM+XX34ZhYWF2LFjB9ra2pCRkYGioiLRI2zd3d3R0dGB+vp6/PTTT6KguF+Dg4PYv38/Vq1ahVmzZol+4uPjUVNTg6ampjH1tXr1amg0Grzwwgtobm5GSUmJcBdwZD5btmzBF198gbNnz6KpqQlHjhyBr68vAMDR0RFSqRTFxcX48ccfceXKldueS6FQwMzMDHFxcWhsbER5eTlefvllxMbGQiaT3eeqMMaYdpxNnE2cTezX8CaLTWgRERHo7+/HtGnTRG9M4eHh6O3tFR6nC9y4+5STk4P8/HzMnDkTaWlpt/0Vv0KhgFKpRExMDKRSqVAukUhQWFiIsLAwrFu3DjNmzMCzzz6Lzs7O274xxsfHY+/evcjOzoa/vz/Cw8ORk5NzV3cL582bh927d2PHjh0ICAhAcXExXn31VdFnx//whz9g8eLFiIiIgIODAw4ePDjm/n9NQUEBLl68iJiYmFF1vr6+8PX1HfMdQ2tra3z55Zeor69HYGAgNm/ejC1btgCAMB8TExO88cYbmD17NsLCwmBoaIhDhw4BAIyMjLBz505kZGTA2dkZy5Ytu+25zM3NUVJSgkuXLuGxxx7D8uXLsWjRIvztb3+72yVgjLEx42zibOJsYr9GQkQ03oNgjI32/PPP48yZM6isrNR53+7u7khMTERiYqLO+9bmwIEDWLduHa5cuSL6H4eJLicnB4mJiVCr1eM9FMYYmxA4m8YfZ9OjgR98wdgE8f777+P3v/89LCwsUFRUhH379on+cKSuJScn480338QPP/yg878e//e//x2enp5wcXGBUqlEcnIynnnmmUcqxCwtLTE0NCS6Y8sYY/qGs2li4Wx6dPAmi7EJora2Fu+++y56e3vh6emJnTt3Ij4+/oGc6+jRoxgcHAQAWFlZ6bx/lUqFLVu2QKVSwcnJCStWrEBqaqrOz/Mg1dfXAxj9JC7GGNMnnE0TC2fTo4M/LsgYY4wxxhhjOsQPvmCMMcYYY4wxHeJNFmOMMcYYY4zpEG+yGGOMMcYYY0yHeJPFGGOMMcYYYzrEmyzGGGOMMcYY0yHeZDHGGGOMMcaYDvEmizHGGGOMMcZ0iDdZjDHGGGOMMaZD/weUYxE9PUepnAAAAABJRU5ErkJggg==", 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", 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" ] @@ -497,7 +500,7 @@ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", "\n", - "spectra = rubixdata.stars.datacube # Spectra of all stars\n", + "spectra = rubixdata#.stars.datacube # Spectra of all stars\n", "spectra_sharded = shard_rubixdata # Spectra of all stars\n", "#print(spectra.shape)\n", "\n", @@ -507,12 +510,14 @@ "plt.xlabel(\"Wavelength [Angstrom]\")\n", "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", "plt.plot(wave, spectra[12,12,:])\n", + "plt.plot(wave, spectra[8,12,:])\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.title(\"Rubix Sharded\")\n", "plt.xlabel(\"Wavelength [Angstrom]\")\n", "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", "plt.plot(wave, spectra_sharded[12,12,:])\n", + "plt.plot(wave, spectra_sharded[8,12,:])\n", "\n", "plt.show()" ] @@ -526,12 +531,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -543,7 +548,8 @@ "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", - "visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", + "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", + "visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", "sharded_visible_spectra = shard_rubixdata[ :, :, visible_indices[0]]\n", "#visible_spectra.shape\n", "\n", diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 8d0b18a8..828e343d 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -12,6 +12,7 @@ from beartype import beartype as typechecker from jax import block_until_ready from jax.experimental import shard_map +from types import SimpleNamespace from jax.sharding import NamedSharding from jax.sharding import Mesh, PartitionSpec as P from jaxtyping import jaxtyped @@ -286,7 +287,8 @@ def _shard_pipeline(sharded_rubixdata): local_cube = out_local.stars.datacube # shape (25,25,5994) # in‐XLA all‐reduce across the "data" axis: #full_cube = lax.psum(local_cube, axis_name="data") - return local_cube # replicated on each device + #summed_cube = lax.psum(local_cube, axis_name="data") + return local_cube # replicated on each device shard_pipeline = pjit( _shard_pipeline, # the function to compile @@ -295,63 +297,182 @@ def _shard_pipeline(sharded_rubixdata): ) with mesh: - partial_cubes = shard_pipeline(inputdata) - partial_cubes = jax.block_until_ready(partial_cubes) + full_cube = shard_pipeline(inputdata) + #full_cube = lax.psum(partial_cubes, axis_name="data") + #partial_cubes = jax.block_until_ready(partial_cubes) + full_cube = jax.block_until_ready(full_cube) + time_end = time.time() + self.logger.info( + "Pipeline run completed in %.2f seconds.", time_end - time_start + ) #final_cube = jnp.sum(partial_cubes, axis=0) - return partial_cubes + return full_cube + + + def run_sharded_chunked(self, inputdata): + """ + Runs the pipeline on sharded input data in parallel using jax.shard_map. + It splits the particle arrays (e.g. under stars and gas) into shards, runs + the compiled pipeline on each shard, and then combines the resulting datacubes. + + Parameters + ---------- + inputdata : object + Data prepared from the `prepare_data` method. + shard_size : int + Number of particles per shard. + Returns + ------- + jax.numpy.ndarray + The final datacube combined from all shards. """ - @partial(jax.jit, - #how inputs ARE sharded when the function is called - in_shardings = (rubix_spec,), - out_shardings = replicate_3d, + time_start = time.time() + # Assemble and compile the pipeline as before. + functions = self._get_pipeline_functions() + self._pipeline = pipeline.LinearTransformerPipeline( + self.pipeline_config, functions ) + self.logger.info("Assembling the pipeline...") + self._pipeline.assemble() + self.logger.info("Compiling the expressions...") + self.func = self._pipeline.compile_expression() - def shard_pipeline(sharded_rubixdata): - out_local = self.func(sharded_rubixdata) - # locally computed partial cube - local_cube = out_local.stars.datacube - # reduce across devices - return local_cube + devices = jax.devices() + num_devices = len(devices) + self.logger.info("Number of devices: %d", num_devices) - with mesh: - # `shard_pipeline` returns a GDA with shape (num_devices, 25,25,5994) - partial_cubes = shard_pipeline(inputdata) - # now in host‐land you can simply - #full_cube = jnp.sum(partial_cubes, axis=0) + mesh = Mesh(devices, ("data",)) - time_end = time.time() - self.logger.info( - "Pipeline run completed in %.2f seconds.", time_end - time_start - ) + # — sharding specs by rank — + replicate_0d = NamedSharding(mesh, P()) # for scalars + replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays + shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) + shard_1d = NamedSharding(mesh, P("data")) # for (N,) + replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube - return partial_cubes - """ - - """ + # — 1) allocate empty instances — + galaxy_spec = object.__new__(Galaxy) + stars_spec = object.__new__(StarsData) + gas_spec = object.__new__(GasData) + rubix_spec = object.__new__(RubixData) + + # — 2) assign NamedSharding to each field — + # galaxy + galaxy_spec.redshift = replicate_0d + galaxy_spec.center = replicate_1d + galaxy_spec.halfmassrad_stars = replicate_0d + + # stars + stars_spec.coords = shard_2d + stars_spec.velocity = shard_2d + stars_spec.mass = shard_1d + stars_spec.age = shard_1d + stars_spec.metallicity = shard_1d + stars_spec.pixel_assignment = shard_1d + stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + stars_spec.mask = shard_1d + stars_spec.spectra = shard_2d + stars_spec.datacube = replicate_3d + + # gas (same idea) + gas_spec.coords = shard_2d + gas_spec.velocity = shard_2d + gas_spec.mass = shard_1d + gas_spec.density = shard_1d + gas_spec.internal_energy = shard_1d + gas_spec.metallicity = shard_1d + gas_spec.metals = shard_1d + gas_spec.sfr = shard_1d + gas_spec.electron_abundance = shard_1d + gas_spec.pixel_assignment = shard_1d + gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + gas_spec.mask = shard_1d + gas_spec.spectra = shard_2d + gas_spec.datacube = replicate_3d + + # — link them up — + rubix_spec.galaxy = galaxy_spec + rubix_spec.stars = stars_spec + rubix_spec.gas = gas_spec + + #if the particle number is not modulo the device number, we have to padd a few empty particles + # to make it work + # this is a bit of a hack, but it works + n = inputdata.stars.coords.shape[0] + pad = (num_devices - (n % num_devices)) % num_devices + + if pad: + # pad along the first axis + inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0,pad),(0,0))) + inputdata.stars.velocity = jnp.pad(inputdata.stars.velocity, ((0,pad),(0,0))) + inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0,pad))) + inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0,pad))) + inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0,pad))) + + + # Precompute all static sizes on the host + telescope = get_telescope(self.user_config) + num_spaxels = int(telescope.sbin) + n_wave = int(telescope.wave_seq.shape[0]) + n_stars = int(inputdata.stars.coords.shape[0]) + chunk_size = 1000 #* num_devices + n_chunks = (n_stars + chunk_size - 1) // chunk_size + total_len = n_chunks * chunk_size + + pad_amt = total_len - n_stars + if pad_amt: + pad_width_2d = ((0, pad_amt), (0, 0)) + pad_width_1d = ((0, pad_amt),) + inputdata.stars.coords = jnp.pad(inputdata.stars.coords, pad_width_2d) + inputdata.stars.velocity = jnp.pad(inputdata.stars.velocity, pad_width_2d) + inputdata.stars.mass = jnp.pad(inputdata.stars.mass, pad_width_1d) + inputdata.stars.age = jnp.pad(inputdata.stars.age, pad_width_1d) + inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, pad_width_1d) + inputdata.stars.pixel_assignment = jnp.pad(inputdata.stars.pixel_assignment, pad_width_1d) + + # Helper to slice RubixData along axis 0 + def slice_data(rubixdata, start): + def slicer(x): + if isinstance(x, jax.Array) and x.shape and x.shape[0] == total_len: + return lax.dynamic_slice_in_dim(x, start, chunk_size, axis=0) + else: + return x + return jax.tree_util.tree_map(slicer, rubixdata) + + # Sharded pipeline function def _shard_pipeline(sharded_rubixdata): - out_local = self.func(sharded_rubixdata) - local_cube = out_local.stars.datacube - # this requires that you actually be in a mesh context with an axis_name="data" - full_cube = lax.psum(local_cube, axis_name="data") - return full_cube + out_local = self.func(sharded_rubixdata) + local_cube = out_local.stars.datacube # shape (25,25,5994) + return local_cube # replicated on each device - # compile it + # Compile the sharded pipeline shard_pipeline = pjit( - _shard_pipeline, # <— the function - in_shardings = (rubix_spec,), - out_shardings = (replicate_3d,), + _shard_pipeline, # the function to compile + in_shardings=(rubix_spec,), + out_shardings=replicate_3d, ) - # then inside your mesh: + # Process the inputdata in 4 chunks and sum the partial cubes with mesh: - final_datacube = shard_pipeline(inputdata) - - return final_datacube - """ + full_cube = jnp.zeros((num_spaxels, num_spaxels, n_wave), jnp.float32) + for i in range(n_chunks): # Process 4 chunks + #print(f"Processing chunk {i + 1}/{n_chunks}...") + start = i * (n_stars // n_chunks) + chunk_data = slice_data(inputdata, start) + partial_cube = shard_pipeline(chunk_data) + full_cube += partial_cube + full_cube = jax.block_until_ready(full_cube) + + time_end = time.time() + self.logger.info("Pipeline run completed in %.2f seconds.", time_end - time_start) + + return full_cube + + def gradient(self): """ This function will calculate the gradient of the pipeline, but is not implemented. From ceeb4dc1d67446cedea27c35fba31ab2def065fe Mon Sep 17 00:00:00 2001 From: anschaible Date: Mon, 28 Apr 2025 10:53:24 +0200 Subject: [PATCH 12/76] current sharding version, still not fully working --- ...x_pipeline_single_function_shard_map.ipynb | 409 +++++++++++++----- rubix/core/pipeline.py | 5 +- 2 files changed, 314 insertions(+), 100 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 771c8491..70c74674 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -46,7 +46,7 @@ "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '6,7,8,9'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '9'\n", "\n", "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", @@ -121,24 +121,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-25 14:18:34,943 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-04-25 14:18:34,945 - rubix - INFO - Rubix version: 0.0.post417+g76e9abf.d20250424\n", - "2025-04-25 14:18:34,945 - rubix - INFO - JAX version: 0.6.0\n", - "2025-04-25 14:18:34,946 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -169,7 +152,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 200000,\n", + " \"subset_size\": 1000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -295,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -321,55 +304,55 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-04-25 14:18:35,850 - rubix - INFO - Getting rubix data...\n", - "2025-04-25 14:18:35,851 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-25 14:18:35,918 - rubix - INFO - Centering stars particles\n", - "2025-04-25 14:18:38,066 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-04-25 14:18:38,067 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-04-25 14:18:38,067 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-25 14:18:38,068 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-25 14:18:38,069 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-25 14:18:38,071 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-25 14:29:13,491 - rubix - INFO - Getting rubix data...\n", + "2025-04-25 14:29:13,492 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-25 14:29:13,549 - rubix - INFO - Centering stars particles\n", + "2025-04-25 14:29:15,558 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-04-25 14:29:15,559 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-04-25 14:29:15,560 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-25 14:29:15,560 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-25 14:29:15,561 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-25 14:29:15,563 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:38,088 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:29:15,580 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:38,534 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-25 14:29:16,010 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:38,548 - rubix - INFO - Getting cosmology...\n", - "2025-04-25 14:18:38,636 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-25 14:18:38,733 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-25 14:29:16,028 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:29:16,119 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-25 14:29:16,208 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:38,881 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-25 14:29:16,365 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:38,898 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:29:16,385 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:39,487 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-25 14:18:39,488 - rubix - INFO - Compiling the expressions...\n", - "2025-04-25 14:18:39,489 - rubix - INFO - Number of devices: 4\n", - "2025-04-25 14:18:39,491 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-25 14:18:39,596 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-25 14:18:39,602 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-25 14:18:39,629 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-25 14:18:39,630 - rubix - DEBUG - Input shapes: Metallicity: 2000, Age: 2000\n", - "2025-04-25 14:18:39,766 - rubix - DEBUG - Calculation Finished! Spectra shape: (2000, 5994)\n", - "2025-04-25 14:18:39,767 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-25 14:18:39,772 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-25 14:18:39,773 - rubix - DEBUG - Doppler Shifted SSP Wave: (2000, 5994)\n", - "2025-04-25 14:18:39,774 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-25 14:18:39,840 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-25 14:18:39,842 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-25 14:18:39,843 - rubix - INFO - Convolving with PSF...\n", - "2025-04-25 14:18:39,849 - rubix - INFO - Convolving with LSF...\n", - "2025-04-25 14:18:39,855 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-04-25 14:18:57,533 - rubix - INFO - Pipeline run completed in 19.47 seconds.\n" + "2025-04-25 14:29:16,972 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-25 14:29:16,973 - rubix - INFO - Compiling the expressions...\n", + "2025-04-25 14:29:16,974 - rubix - INFO - Number of devices: 4\n", + "2025-04-25 14:29:16,976 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-25 14:29:17,097 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-25 14:29:17,104 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-25 14:29:17,131 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-25 14:29:17,132 - rubix - DEBUG - Input shapes: Metallicity: 2000, Age: 2000\n", + "2025-04-25 14:29:17,274 - rubix - DEBUG - Calculation Finished! Spectra shape: (2000, 5994)\n", + "2025-04-25 14:29:17,275 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-25 14:29:17,280 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-25 14:29:17,281 - rubix - DEBUG - Doppler Shifted SSP Wave: (2000, 5994)\n", + "2025-04-25 14:29:17,282 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-25 14:29:17,349 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-25 14:29:17,352 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-25 14:29:17,353 - rubix - INFO - Convolving with PSF...\n", + "2025-04-25 14:29:17,356 - rubix - INFO - Convolving with LSF...\n", + "2025-04-25 14:29:17,362 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-04-25 14:29:35,129 - rubix - INFO - Pipeline run completed in 19.57 seconds.\n" ] } ], @@ -382,64 +365,294 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-25 14:18:57,576 - rubix - INFO - Getting rubix data...\n", - "2025-04-25 14:18:57,578 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-25 14:18:57,605 - rubix - INFO - Centering stars particles\n", - "2025-04-25 14:18:57,632 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-04-25 14:18:57,633 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-04-25 14:18:57,635 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-25 14:18:57,635 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-25 14:18:57,636 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-25 14:18:57,639 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-25 14:30:33,937 - rubix - INFO - Getting rubix data...\n", + "2025-04-25 14:30:33,940 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-25 14:30:33,982 - rubix - INFO - Centering stars particles\n", + "2025-04-25 14:30:34,350 - rubix - WARNING - The Subset value is set in config. Using only subset of size 200000 for stars\n", + "2025-04-25 14:30:34,351 - rubix - INFO - Data loaded with 200000 star particles and 0 gas particles.\n", + "2025-04-25 14:30:34,352 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-25 14:30:34,352 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-25 14:30:34,353 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-25 14:30:34,354 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:57,658 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:30:34,371 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:57,676 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-25 14:30:34,391 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:57,691 - rubix - INFO - Getting cosmology...\n", - "2025-04-25 14:18:57,781 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-25 14:18:57,838 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-25 14:30:34,407 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:30:34,472 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-25 14:30:34,531 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:57,911 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-25 14:30:34,607 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:57,933 - rubix - INFO - Getting cosmology...\n", + "2025-04-25 14:30:34,626 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:57,969 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-25 14:18:57,969 - rubix - INFO - Compiling the expressions...\n", - "2025-04-25 14:18:57,973 - rubix - INFO - Number of devices: 4\n", + "2025-04-25 14:30:34,664 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-25 14:30:34,666 - rubix - INFO - Compiling the expressions...\n", + "2025-04-25 14:30:34,667 - rubix - INFO - Number of devices: 4\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:18:58,170 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-25 14:18:58,264 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-25 14:18:58,269 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-25 14:18:58,295 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-25 14:18:58,296 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-25 14:18:58,551 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-25 14:18:58,552 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-25 14:18:58,557 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-25 14:18:58,558 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-25 14:18:58,558 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-25 14:18:58,589 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-25 14:18:58,592 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-25 14:18:58,592 - rubix - INFO - Convolving with PSF...\n", - "2025-04-25 14:18:58,596 - rubix - INFO - Convolving with LSF...\n", - "2025-04-25 14:18:58,599 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-04-25 14:19:14,194 - rubix - INFO - Pipeline run completed in 16.56 seconds.\n" + "2025-04-25 14:30:34,777 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-25 14:30:34,877 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-25 14:30:34,880 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-25 14:30:34,882 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-25 14:30:34,883 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing chunk 1/200...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-25 14:30:34,885 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-25 14:30:34,886 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-25 14:30:34,889 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-25 14:30:34,889 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-25 14:30:34,889 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-25 14:30:34,899 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-25 14:30:34,901 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-25 14:30:34,902 - rubix - INFO - Convolving with PSF...\n", + "2025-04-25 14:30:34,905 - rubix - INFO - Convolving with LSF...\n", + "2025-04-25 14:30:34,911 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing chunk 2/200...\n", + "Processing chunk 3/200...\n", + "Processing chunk 4/200...\n", + "Processing chunk 5/200...\n", + 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"cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -482,12 +695,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -531,12 +744,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 828e343d..b22e3cbd 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -297,8 +297,8 @@ def _shard_pipeline(sharded_rubixdata): ) with mesh: - full_cube = shard_pipeline(inputdata) - #full_cube = lax.psum(partial_cubes, axis_name="data") + partial_cubes = shard_pipeline(inputdata) + full_cube = lax.psum(partial_cubes, axis_name="data") #partial_cubes = jax.block_until_ready(partial_cubes) full_cube = jax.block_until_ready(full_cube) @@ -433,6 +433,7 @@ def run_sharded_chunked(self, inputdata): inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, pad_width_1d) inputdata.stars.pixel_assignment = jnp.pad(inputdata.stars.pixel_assignment, pad_width_1d) + # Helper to slice RubixData along axis 0 def slice_data(rubixdata, start): def slicer(x): From 3d28d2a37c62f3d08d1e34aea7dfafc3f1c50fd6 Mon Sep 17 00:00:00 2001 From: anschaible Date: Mon, 28 Apr 2025 11:39:52 +0200 Subject: [PATCH 13/76] implement shard map according to Leonard hints --- ...x_pipeline_single_function_shard_map.ipynb | 411 +++++------------- rubix/core/pipeline.py | 41 +- 2 files changed, 127 insertions(+), 325 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 70c74674..b4843777 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -28,14 +28,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)]\n" + "[CudaDevice(id=0), CudaDevice(id=1)]\n" ] } ], @@ -46,7 +46,7 @@ "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '9'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '8, 9'\n", "\n", "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", @@ -119,9 +119,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-04-28 11:38:25,760 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-04-28 11:38:25,760 - rubix - INFO - Rubix version: 0.0.post417+g76e9abf.d20250424\n", + "2025-04-28 11:38:25,761 - rubix - INFO - JAX version: 0.6.0\n", + "2025-04-28 11:38:25,761 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -278,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -304,55 +321,55 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-04-25 14:29:13,491 - rubix - INFO - Getting rubix data...\n", - "2025-04-25 14:29:13,492 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-25 14:29:13,549 - rubix - INFO - Centering stars particles\n", - "2025-04-25 14:29:15,558 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-04-25 14:29:15,559 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-04-25 14:29:15,560 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-25 14:29:15,560 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-25 14:29:15,561 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-25 14:29:15,563 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-28 11:38:26,602 - rubix - INFO - Getting rubix data...\n", + "2025-04-28 11:38:26,604 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-28 11:38:26,667 - rubix - INFO - Centering stars particles\n", + "2025-04-28 11:38:28,769 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", + "2025-04-28 11:38:28,770 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", + "2025-04-28 11:38:28,771 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-28 11:38:28,771 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-28 11:38:28,773 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-28 11:38:28,776 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:29:15,580 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 11:38:28,792 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:29:16,010 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-28 11:38:29,281 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:29:16,028 - rubix - INFO - Getting cosmology...\n", - "2025-04-25 14:29:16,119 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-25 14:29:16,208 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-28 11:38:29,297 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 11:38:29,391 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-28 11:38:29,480 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:29:16,365 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-28 11:38:29,644 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:29:16,385 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 11:38:29,664 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:29:16,972 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-25 14:29:16,973 - rubix - INFO - Compiling the expressions...\n", - "2025-04-25 14:29:16,974 - rubix - INFO - Number of devices: 4\n", - "2025-04-25 14:29:16,976 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-25 14:29:17,097 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-25 14:29:17,104 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-25 14:29:17,131 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-25 14:29:17,132 - rubix - DEBUG - Input shapes: Metallicity: 2000, Age: 2000\n", - "2025-04-25 14:29:17,274 - rubix - DEBUG - Calculation Finished! Spectra shape: (2000, 5994)\n", - "2025-04-25 14:29:17,275 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-25 14:29:17,280 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-25 14:29:17,281 - rubix - DEBUG - Doppler Shifted SSP Wave: (2000, 5994)\n", - "2025-04-25 14:29:17,282 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-25 14:29:17,349 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-25 14:29:17,352 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-25 14:29:17,353 - rubix - INFO - Convolving with PSF...\n", - "2025-04-25 14:29:17,356 - rubix - INFO - Convolving with LSF...\n", - "2025-04-25 14:29:17,362 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-04-25 14:29:35,129 - rubix - INFO - Pipeline run completed in 19.57 seconds.\n" + "2025-04-28 11:38:30,294 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-28 11:38:30,295 - rubix - INFO - Compiling the expressions...\n", + "2025-04-28 11:38:30,297 - rubix - INFO - Number of devices: 2\n", + "2025-04-28 11:38:30,566 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-28 11:38:30,696 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-28 11:38:30,703 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-28 11:38:30,737 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-28 11:38:30,738 - rubix - DEBUG - Input shapes: Metallicity: 500, Age: 500\n", + "2025-04-28 11:38:30,885 - rubix - DEBUG - Calculation Finished! Spectra shape: (500, 5994)\n", + "2025-04-28 11:38:30,887 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-28 11:38:30,894 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-28 11:38:30,895 - rubix - DEBUG - Doppler Shifted SSP Wave: (500, 5994)\n", + "2025-04-28 11:38:30,896 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-28 11:38:30,980 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-28 11:38:30,983 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-28 11:38:30,984 - rubix - INFO - Convolving with PSF...\n", + "2025-04-28 11:38:30,989 - rubix - INFO - Convolving with LSF...\n", + "2025-04-28 11:38:30,998 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-04-28 11:38:48,443 - rubix - INFO - Pipeline run completed in 19.67 seconds.\n" ] } ], @@ -365,294 +382,64 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-25 14:30:33,937 - rubix - INFO - Getting rubix data...\n", - "2025-04-25 14:30:33,940 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-25 14:30:33,982 - rubix - INFO - Centering stars particles\n", - "2025-04-25 14:30:34,350 - rubix - WARNING - The Subset value is set in config. Using only subset of size 200000 for stars\n", - "2025-04-25 14:30:34,351 - rubix - INFO - Data loaded with 200000 star particles and 0 gas particles.\n", - "2025-04-25 14:30:34,352 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-25 14:30:34,352 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-25 14:30:34,353 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-25 14:30:34,354 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-28 11:38:48,457 - rubix - INFO - Getting rubix data...\n", + "2025-04-28 11:38:48,462 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-28 11:38:48,489 - rubix - INFO - Centering stars particles\n", + "2025-04-28 11:38:48,526 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", + "2025-04-28 11:38:48,527 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", + "2025-04-28 11:38:48,528 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-28 11:38:48,529 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-28 11:38:48,530 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-28 11:38:48,533 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:30:34,371 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 11:38:48,557 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:30:34,391 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-28 11:38:48,580 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:30:34,407 - rubix - INFO - Getting cosmology...\n", - "2025-04-25 14:30:34,472 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-25 14:30:34,531 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-28 11:38:48,597 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 11:38:48,685 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-28 11:38:48,737 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:30:34,607 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-28 11:38:48,809 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:30:34,626 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 11:38:48,826 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:30:34,664 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-25 14:30:34,666 - rubix - INFO - Compiling the expressions...\n", - "2025-04-25 14:30:34,667 - rubix - INFO - Number of devices: 4\n", + "2025-04-28 11:38:48,868 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-28 11:38:48,869 - rubix - INFO - Compiling the expressions...\n", + "2025-04-28 11:38:48,870 - rubix - INFO - Number of devices: 2\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-25 14:30:34,777 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-25 14:30:34,877 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-25 14:30:34,880 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-25 14:30:34,882 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-25 14:30:34,883 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing chunk 1/200...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-25 14:30:34,885 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-25 14:30:34,886 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-25 14:30:34,889 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-25 14:30:34,889 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-25 14:30:34,889 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-25 14:30:34,899 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-25 14:30:34,901 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-25 14:30:34,902 - rubix - INFO - Convolving with PSF...\n", - "2025-04-25 14:30:34,905 - rubix - INFO - Convolving with LSF...\n", - "2025-04-25 14:30:34,911 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing chunk 2/200...\n", - "Processing chunk 3/200...\n", - "Processing chunk 4/200...\n", - "Processing chunk 5/200...\n", - 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Filtering particles outside the aperture...\n", + "2025-04-28 11:38:49,102 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-28 11:38:49,130 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-28 11:38:49,131 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-28 11:38:49,428 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-28 11:38:49,429 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-28 11:38:49,434 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-28 11:38:49,436 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-28 11:38:49,437 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-28 11:38:49,505 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-28 11:38:49,507 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-28 11:38:49,508 - rubix - INFO - Convolving with PSF...\n", + "2025-04-28 11:38:49,512 - rubix - INFO - Convolving with LSF...\n", + "2025-04-28 11:38:49,518 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-04-28 11:39:05,703 - rubix - INFO - Pipeline run completed in 17.17 seconds.\n" ] } ], @@ -674,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -695,12 +482,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -744,12 +531,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index b22e3cbd..4a85da96 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -5,13 +5,14 @@ import jax import jax.numpy as jnp from jax.tree_util import tree_flatten, tree_unflatten +from jax.tree_util import tree_map import dataclasses # For shard_map and device mesh. import numpy as np from beartype import beartype as typechecker from jax import block_until_ready -from jax.experimental import shard_map +from jax.experimental.shard_map import shard_map from types import SimpleNamespace from jax.sharding import NamedSharding from jax.sharding import Mesh, PartitionSpec as P @@ -212,7 +213,7 @@ def run_sharded(self, inputdata): num_devices = len(devices) self.logger.info("Number of devices: %d", num_devices) - mesh = Mesh(devices, ("data",)) + mesh = Mesh(devices, axis_names = ("data",)) # — sharding specs by rank — replicate_0d = NamedSharding(mesh, P()) # for scalars @@ -266,6 +267,12 @@ def run_sharded(self, inputdata): rubix_spec.stars = stars_spec rubix_spec.gas = gas_spec + # 1) Make a pytree of PartitionSpec + partition_spec_tree = tree_map( + lambda s: s.spec if isinstance(s, NamedSharding) else None, + rubix_spec + ) + #if the particle number is not modulo the device number, we have to padd a few empty particles # to make it work # this is a bit of a hack, but it works @@ -280,27 +287,35 @@ def run_sharded(self, inputdata): inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0,pad))) inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0,pad))) - + inputdata = jax.device_put(inputdata, rubix_spec) + # create the sharded data def _shard_pipeline(sharded_rubixdata): out_local = self.func(sharded_rubixdata) local_cube = out_local.stars.datacube # shape (25,25,5994) # in‐XLA all‐reduce across the "data" axis: #full_cube = lax.psum(local_cube, axis_name="data") - #summed_cube = lax.psum(local_cube, axis_name="data") - return local_cube # replicated on each device + summed_cube = lax.psum(local_cube, axis_name="data") + return summed_cube # replicated on each device - shard_pipeline = pjit( + sharded_pipeline = shard_map( _shard_pipeline, # the function to compile - in_shardings = (rubix_spec,), - out_shardings = replicate_3d, + mesh=mesh, # the mesh to use + in_specs = (partition_spec_tree,), + out_specs = replicate_3d.spec, + check_rep = False, ) - with mesh: - partial_cubes = shard_pipeline(inputdata) - full_cube = lax.psum(partial_cubes, axis_name="data") + #with mesh: + # inputdata = jax.device_put(inputdata, rubix_spec) + #partial_cubes = shard_pipeline(inputdata) + #full_cube = lax.psum(partial_cubes, axis_name="data") #partial_cubes = jax.block_until_ready(partial_cubes) - full_cube = jax.block_until_ready(full_cube) + #full_cube = jax.block_until_ready(full_cube) + + #full_cube = partial_cubes.sum(axis=0) + + sharded_result = sharded_pipeline(inputdata) time_end = time.time() self.logger.info( @@ -308,7 +323,7 @@ def _shard_pipeline(sharded_rubixdata): ) #final_cube = jnp.sum(partial_cubes, axis=0) - return full_cube + return sharded_result def run_sharded_chunked(self, inputdata): From b51e08f30a7ce3191a5bb24c02c27fe7018fc3b1 Mon Sep 17 00:00:00 2001 From: anschaible Date: Mon, 28 Apr 2025 12:13:43 +0200 Subject: [PATCH 14/76] sharding now works, remove for loop through jax.for_i is still missing --- ...x_pipeline_single_function_shard_map.ipynb | 179 ++++++++---------- rubix/core/pipeline.py | 71 ++++--- 2 files changed, 125 insertions(+), 125 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index b4843777..35673c65 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[CudaDevice(id=0), CudaDevice(id=1)]\n" + "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2)]\n" ] } ], @@ -46,7 +46,7 @@ "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '8, 9'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '7, 8, 9'\n", "\n", "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", @@ -119,26 +119,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-28 11:38:25,760 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-04-28 11:38:25,760 - rubix - INFO - Rubix version: 0.0.post417+g76e9abf.d20250424\n", - "2025-04-28 11:38:25,761 - rubix - INFO - JAX version: 0.6.0\n", - "2025-04-28 11:38:25,761 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -169,7 +152,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 1000,\n", + " \"subset_size\": 30000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -295,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -321,55 +304,55 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-04-28 11:38:26,602 - rubix - INFO - Getting rubix data...\n", - "2025-04-28 11:38:26,604 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-28 11:38:26,667 - rubix - INFO - Centering stars particles\n", - "2025-04-28 11:38:28,769 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", - "2025-04-28 11:38:28,770 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", - "2025-04-28 11:38:28,771 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-28 11:38:28,771 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-28 11:38:28,773 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-28 11:38:28,776 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-28 12:08:55,651 - rubix - INFO - Getting rubix data...\n", + "2025-04-28 12:08:55,653 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-28 12:08:55,718 - rubix - INFO - Centering stars particles\n", + "2025-04-28 12:08:57,906 - rubix - WARNING - The Subset value is set in config. Using only subset of size 3000 for stars\n", + "2025-04-28 12:08:57,907 - rubix - INFO - Data loaded with 3000 star particles and 0 gas particles.\n", + "2025-04-28 12:08:57,908 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-28 12:08:57,908 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-28 12:08:57,909 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-28 12:08:57,911 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:28,792 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 12:08:57,928 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:29,281 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-28 12:08:58,411 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:29,297 - rubix - INFO - Getting cosmology...\n", - "2025-04-28 11:38:29,391 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-28 11:38:29,480 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-28 12:08:58,434 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 12:08:58,526 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-28 12:08:58,620 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:29,644 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-28 12:08:58,779 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:29,664 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 12:08:58,800 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:30,294 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-28 11:38:30,295 - rubix - INFO - Compiling the expressions...\n", - "2025-04-28 11:38:30,297 - rubix - INFO - Number of devices: 2\n", - "2025-04-28 11:38:30,566 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-28 11:38:30,696 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-28 11:38:30,703 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-28 11:38:30,737 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-28 11:38:30,738 - rubix - DEBUG - Input shapes: Metallicity: 500, Age: 500\n", - "2025-04-28 11:38:30,885 - rubix - DEBUG - Calculation Finished! Spectra shape: (500, 5994)\n", - "2025-04-28 11:38:30,887 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-28 11:38:30,894 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-28 11:38:30,895 - rubix - DEBUG - Doppler Shifted SSP Wave: (500, 5994)\n", - "2025-04-28 11:38:30,896 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-28 11:38:30,980 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-28 11:38:30,983 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-28 11:38:30,984 - rubix - INFO - Convolving with PSF...\n", - "2025-04-28 11:38:30,989 - rubix - INFO - Convolving with LSF...\n", - "2025-04-28 11:38:30,998 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-04-28 11:38:48,443 - rubix - INFO - Pipeline run completed in 19.67 seconds.\n" + "2025-04-28 12:08:59,430 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-28 12:08:59,431 - rubix - INFO - Compiling the expressions...\n", + "2025-04-28 12:08:59,432 - rubix - INFO - Number of devices: 3\n", + "2025-04-28 12:08:59,746 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-28 12:08:59,882 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-28 12:08:59,890 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-28 12:08:59,926 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-28 12:08:59,927 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-28 12:09:00,070 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-28 12:09:00,071 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-28 12:09:00,076 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-28 12:09:00,077 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-28 12:09:00,077 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-28 12:09:00,147 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-28 12:09:00,150 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-28 12:09:00,151 - rubix - INFO - Convolving with PSF...\n", + "2025-04-28 12:09:00,155 - rubix - INFO - Convolving with LSF...\n", + "2025-04-28 12:09:00,161 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-04-28 12:09:17,590 - rubix - INFO - Pipeline run completed in 19.68 seconds.\n" ] } ], @@ -382,64 +365,64 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-04-28 11:38:48,457 - rubix - INFO - Getting rubix data...\n", - "2025-04-28 11:38:48,462 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-28 11:38:48,489 - rubix - INFO - Centering stars particles\n", - "2025-04-28 11:38:48,526 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", - "2025-04-28 11:38:48,527 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", - "2025-04-28 11:38:48,528 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-28 11:38:48,529 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-28 11:38:48,530 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-28 11:38:48,533 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-28 12:10:11,524 - rubix - INFO - Getting rubix data...\n", + "2025-04-28 12:10:11,526 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-04-28 12:10:11,563 - rubix - INFO - Centering stars particles\n", + "2025-04-28 12:10:11,892 - rubix - WARNING - The Subset value is set in config. Using only subset of size 30000 for stars\n", + "2025-04-28 12:10:11,894 - rubix - INFO - Data loaded with 30000 star particles and 0 gas particles.\n", + "2025-04-28 12:10:11,895 - rubix - INFO - Setting up the pipeline...\n", + "2025-04-28 12:10:11,895 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-04-28 12:10:11,896 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-04-28 12:10:11,897 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:48,557 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 12:10:11,914 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:48,580 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-04-28 12:10:11,935 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:48,597 - rubix - INFO - Getting cosmology...\n", - "2025-04-28 11:38:48,685 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-28 11:38:48,737 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-28 12:10:11,954 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 12:10:12,038 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-04-28 12:10:12,094 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:48,809 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-04-28 12:10:12,175 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:48,826 - rubix - INFO - Getting cosmology...\n", + "2025-04-28 12:10:12,197 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:48,868 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-28 11:38:48,869 - rubix - INFO - Compiling the expressions...\n", - "2025-04-28 11:38:48,870 - rubix - INFO - Number of devices: 2\n", + "2025-04-28 12:10:12,235 - rubix - INFO - Assembling the pipeline...\n", + "2025-04-28 12:10:12,236 - rubix - INFO - Compiling the expressions...\n", + "2025-04-28 12:10:12,237 - rubix - INFO - Number of devices: 3\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-04-28 11:38:48,985 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-28 11:38:49,096 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-28 11:38:49,102 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-28 11:38:49,130 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-28 11:38:49,131 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-28 11:38:49,428 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-28 11:38:49,429 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-28 11:38:49,434 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-28 11:38:49,436 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-28 11:38:49,437 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-28 11:38:49,505 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-28 11:38:49,507 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-28 11:38:49,508 - rubix - INFO - Convolving with PSF...\n", - "2025-04-28 11:38:49,512 - rubix - INFO - Convolving with LSF...\n", - "2025-04-28 11:38:49,518 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-04-28 11:39:05,703 - rubix - INFO - Pipeline run completed in 17.17 seconds.\n" + "2025-04-28 12:10:12,850 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-04-28 12:10:12,947 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-04-28 12:10:12,951 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-04-28 12:10:12,954 - rubix - INFO - Calculating IFU cube...\n", + "2025-04-28 12:10:12,954 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-04-28 12:10:12,955 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-04-28 12:10:12,956 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-04-28 12:10:12,959 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-04-28 12:10:12,960 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-04-28 12:10:12,960 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-04-28 12:10:12,967 - rubix - INFO - Calculating Data Cube...\n", + "2025-04-28 12:10:12,969 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-04-28 12:10:12,969 - rubix - INFO - Convolving with PSF...\n", + "2025-04-28 12:10:12,972 - rubix - INFO - Convolving with LSF...\n", + "2025-04-28 12:10:12,976 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-04-28 12:12:57,367 - rubix - INFO - Pipeline run completed in 165.47 seconds.\n" ] } ], @@ -482,12 +465,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -531,12 +514,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 4a85da96..58db9a15 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -294,7 +294,6 @@ def _shard_pipeline(sharded_rubixdata): out_local = self.func(sharded_rubixdata) local_cube = out_local.stars.datacube # shape (25,25,5994) # in‐XLA all‐reduce across the "data" axis: - #full_cube = lax.psum(local_cube, axis_name="data") summed_cube = lax.psum(local_cube, axis_name="data") return summed_cube # replicated on each device @@ -413,11 +412,27 @@ def run_sharded_chunked(self, inputdata): rubix_spec.stars = stars_spec rubix_spec.gas = gas_spec + # 1) Make a pytree of PartitionSpec + partition_spec_tree = tree_map( + lambda s: s.spec if isinstance(s, NamedSharding) else None, + rubix_spec + ) + #if the particle number is not modulo the device number, we have to padd a few empty particles # to make it work # this is a bit of a hack, but it works + telescope = get_telescope(self.user_config) + num_spaxels = int(telescope.sbin) + n_wave = int(telescope.wave_seq.shape[0]) + n_stars = int(inputdata.stars.coords.shape[0]) + chunk_size = 1000 * num_devices + n_chunks = (n_stars + chunk_size - 1) // chunk_size + total_len = n_chunks * chunk_size + + pad_amt = total_len - n_stars + n = inputdata.stars.coords.shape[0] - pad = (num_devices - (n % num_devices)) % num_devices + pad = (num_devices - (n % num_devices)) % num_devices + pad_amt if pad: # pad along the first axis @@ -427,13 +442,13 @@ def run_sharded_chunked(self, inputdata): inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0,pad))) inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0,pad))) - + """ # Precompute all static sizes on the host telescope = get_telescope(self.user_config) num_spaxels = int(telescope.sbin) n_wave = int(telescope.wave_seq.shape[0]) n_stars = int(inputdata.stars.coords.shape[0]) - chunk_size = 1000 #* num_devices + chunk_size = 1000 * num_devices n_chunks = (n_stars + chunk_size - 1) // chunk_size total_len = n_chunks * chunk_size @@ -446,8 +461,7 @@ def run_sharded_chunked(self, inputdata): inputdata.stars.mass = jnp.pad(inputdata.stars.mass, pad_width_1d) inputdata.stars.age = jnp.pad(inputdata.stars.age, pad_width_1d) inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, pad_width_1d) - inputdata.stars.pixel_assignment = jnp.pad(inputdata.stars.pixel_assignment, pad_width_1d) - + """ # Helper to slice RubixData along axis 0 def slice_data(rubixdata, start): @@ -458,30 +472,33 @@ def slicer(x): return x return jax.tree_util.tree_map(slicer, rubixdata) - # Sharded pipeline function + inputdata = jax.device_put(inputdata, rubix_spec) + + # create the sharded data def _shard_pipeline(sharded_rubixdata): - out_local = self.func(sharded_rubixdata) - local_cube = out_local.stars.datacube # shape (25,25,5994) - return local_cube # replicated on each device - - # Compile the sharded pipeline - shard_pipeline = pjit( - _shard_pipeline, # the function to compile - in_shardings=(rubix_spec,), - out_shardings=replicate_3d, + out_local = self.func(sharded_rubixdata) + local_cube = out_local.stars.datacube # shape (25,25,5994) + # in‐XLA all‐reduce across the "data" axis: + summed_cube = lax.psum(local_cube, axis_name="data") + return summed_cube # replicated on each device + + sharded_pipeline = shard_map( + _shard_pipeline, # the function to compile + mesh=mesh, # the mesh to use + in_specs = (partition_spec_tree,), + out_specs = replicate_3d.spec, + check_rep = False, ) - # Process the inputdata in 4 chunks and sum the partial cubes - with mesh: - full_cube = jnp.zeros((num_spaxels, num_spaxels, n_wave), jnp.float32) - for i in range(n_chunks): # Process 4 chunks - #print(f"Processing chunk {i + 1}/{n_chunks}...") - start = i * (n_stars // n_chunks) - chunk_data = slice_data(inputdata, start) - partial_cube = shard_pipeline(chunk_data) - full_cube += partial_cube - - full_cube = jax.block_until_ready(full_cube) + full_cube = jnp.zeros((num_spaxels, num_spaxels, n_wave), jnp.float32) + for i in range(n_chunks): # Process 4 chunks + #print(f"Processing chunk {i + 1}/{n_chunks}...") + start = i * (n_stars // n_chunks) + chunk_data = slice_data(inputdata, start) + partial_cube = sharded_pipeline(chunk_data) + full_cube += partial_cube + + full_cube = jax.block_until_ready(full_cube) time_end = time.time() self.logger.info("Pipeline run completed in %.2f seconds.", time_end - time_start) From 53ed291c3d5427f697409664cd325a28b3c4bd17 Mon Sep 17 00:00:00 2001 From: Harald Mack Date: Mon, 5 May 2025 14:09:53 +0200 Subject: [PATCH 15/76] work on sharding experiments --- ...x_pipeline_single_function_shard_map.ipynb | 563 ++++++++++++------ rubix/core/data.py | 28 +- 2 files changed, 382 insertions(+), 209 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 35673c65..fa82cf1a 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,18 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Logical cores: 72\n", - "multiprocessing.cpu_count(): 72\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import os\n", "import multiprocessing\n", @@ -28,17 +28,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2)]\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -46,8 +38,9 @@ "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '7, 8, 9'\n", + "# os.environ['CUDA_VISIBLE_DEVICES'] = '7, 8, 9'\n", "\n", + "# for making sure that JAX doesnt'consume all memory at once\n", "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", "import jax\n", @@ -59,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -68,7 +61,8 @@ "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" + "os.environ['SPS_HOME'] = '/home/hmack/.cache/fsps'\n", + "os.environ['ILLUSTRIS_API_KEY'] = '17c621e8278dcedebc7099b0861105c2'" ] }, { @@ -77,7 +71,7 @@ "source": [ "# RUBIX pipeline\n", "\n", - "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", + "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execute the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline on multiple machines. To see, how the pipeline is executed in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", "\n", "## How to use the Pipeline\n", "1) Define a `config`\n", @@ -96,13 +90,13 @@ "\n", "For the `config` you can choose the following options:\n", "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", - "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", + "- `logger`: RUBIX has implemented a logger to report to the user, what is happening during the pipeline execution and give warnings\n", "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", - "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", + "- `data - load_galaxy_args - reuse`: if True, if in the save_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", "- `simulation - name`: currently only IllustrisTNG is supported\n", "- `simulation - args - path`: where the data is stored and how the file will be named\n", @@ -119,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -127,6 +121,7 @@ "import matplotlib.pyplot as plt\n", "from rubix.core.pipeline import RubixPipeline \n", "import os\n", + "\n", "config = {\n", " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", @@ -176,7 +171,6 @@ " {\"dist_z\": 0.1,\n", " \"rotation\": {\"type\": \"edge-on\"},\n", " },\n", - " \n", " \"ssp\": {\n", " \"template\": {\n", " \"name\": \"FSPS\"\n", @@ -219,19 +213,16 @@ " depends_on: filter_particles\n", " args: []\n", " kwargs: {}\n", - "\n", " reshape_data:\n", " name: reshape_data\n", " depends_on: spaxel_assignment\n", " args: []\n", " kwargs: {}\n", - "\n", " calculate_spectra:\n", " name: calculate_spectra\n", " depends_on: reshape_data\n", " args: []\n", " kwargs: {}\n", - "\n", " scale_spectrum_by_mass:\n", " name: scale_spectrum_by_mass\n", " depends_on: calculate_spectra\n", @@ -264,7 +255,7 @@ " kwargs: {}\n", "```\n", "\n", - "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" + "There is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" ] }, { @@ -278,18 +269,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config)" @@ -297,135 +279,348 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-28 12:08:55,651 - rubix - INFO - Getting rubix data...\n", - "2025-04-28 12:08:55,653 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-28 12:08:55,718 - rubix - INFO - Centering stars particles\n", - "2025-04-28 12:08:57,906 - rubix - WARNING - The Subset value is set in config. Using only subset of size 3000 for stars\n", - "2025-04-28 12:08:57,907 - rubix - INFO - Data loaded with 3000 star particles and 0 gas particles.\n", - "2025-04-28 12:08:57,908 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-28 12:08:57,908 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-28 12:08:57,909 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-28 12:08:57,911 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:08:57,928 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:08:58,411 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:08:58,434 - rubix - INFO - Getting cosmology...\n", - "2025-04-28 12:08:58,526 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-28 12:08:58,620 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:08:58,779 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:08:58,800 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:08:59,430 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-28 12:08:59,431 - rubix - INFO - Compiling the expressions...\n", - "2025-04-28 12:08:59,432 - rubix - INFO - Number of devices: 3\n", - "2025-04-28 12:08:59,746 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-28 12:08:59,882 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-28 12:08:59,890 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-28 12:08:59,926 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-28 12:08:59,927 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-28 12:09:00,070 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-28 12:09:00,071 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-28 12:09:00,076 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-28 12:09:00,077 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-28 12:09:00,077 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-28 12:09:00,147 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-28 12:09:00,150 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-28 12:09:00,151 - rubix - INFO - Convolving with PSF...\n", - "2025-04-28 12:09:00,155 - rubix - INFO - Convolving with LSF...\n", - "2025-04-28 12:09:00,161 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-04-28 12:09:17,590 - rubix - INFO - Pipeline run completed in 19.68 seconds.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", - "inputdata = pipe.prepare_data()\n", + "inputdata = pipe.prepare_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.galaxy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "there is a type instability here, and this number is also massively too low." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.galaxy.redshift, type(inputdata.galaxy.redshift), inputdata.galaxy.redshift.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.galaxy.center, type(inputdata.galaxy.center), inputdata.galaxy.center.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.galaxy.halfmassrad_stars, type(inputdata.galaxy.halfmassrad_stars), inputdata.galaxy.halfmassrad_stars.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.stars.coords, type(inputdata.stars.coords), inputdata.stars.coords.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.stars.velocity, type(inputdata.stars.velocity), inputdata.stars.velocity.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.stars.metallicity, type(inputdata.stars.metallicity), inputdata.stars.metallicity.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.stars.spectra, type(inputdata.stars.spectra), inputdata.stars.spectra.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.stars.mask, type(inputdata.stars.mask), inputdata.stars.mask.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.stars.pixel_assignment, type(inputdata.stars.pixel_assignment), inputdata.stars.pixel_assignment.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.stars.age, type(inputdata.stars.age), inputdata.stars.age.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.gas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.gas.coords, type(inputdata.gas.coords), inputdata.gas.coords.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.galaxy.redshift = jnp.float32(inputdata.galaxy.redshift)\n", + "type(inputdata.galaxy.redshift)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The below needs to be made part of the data system itself, we don't want this to ever land in numpy. It also needs to be collated and put into a somewhat useful shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.galaxy.redshift = jnp.float32(inputdata.galaxy.redshift)\n", + "inputdata.galaxy.halfmassrad_stars = jnp.array(inputdata.galaxy.halfmassrad_stars, dtype=jnp.float32)\n", + "inputdata.galaxy.center = jnp.array(inputdata.galaxy.center, dtype=jnp.float32)\n", + "\n", + "inputdata.stars.coords = jnp.array(inputdata.stars.coords, dtype=jnp.float32)\n", + "inputdata.stars.age = jnp.array(inputdata.stars.age, dtype=jnp.float32)\n", + "inputdata.stars.velocity = jnp.array(inputdata.stars.velocity, dtype=jnp.float32)\n", + "inputdata.stars.metallicity = jnp.array(inputdata.stars.metallicity, dtype=jnp.float32)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputdata.galaxy.center" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = jnp.zeros((30000, 13), dtype=jnp.float32)\n", + "\n", + "# stars properties\n", + "data = data.at[:, 0:3].set(inputdata.stars.coords)\n", + "data = data.at[:, 3:6].set(inputdata.stars.velocity)\n", + "data = data.at[:, 6].set(inputdata.stars.metallicity)\n", + "data = data.at[:, 7].set(inputdata.stars.age)\n", + "\n", + "# galaxy properties\n", + "data = data.at[:, 8].set(inputdata.galaxy.halfmassrad_stars)\n", + "data = data.at[:, 9].set(inputdata.galaxy.redshift)\n", + "data = data.at[:, 10:13].set(inputdata.galaxy.center)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def stars(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Stars function to be used in the pipeline.\n", + " \"\"\"\n", + " # Perform some operations on the data\n", + " # For example, let's just return the data as is\n", + " return data[:, 0:8]\n", + "\n", + "def galaxy(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Galaxy function to be used in the pipeline.\n", + " \"\"\"\n", + " # Perform some operations on the data\n", + " # For example, let's just return the data as is\n", + " return data[:, 8:13]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def coords(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Coords function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[:, 0:3]\n", + "\n", + "def velocity(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Velocity function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[:, 3:6]\n", + "\n", + "def metallicity(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Metallicity function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[:, 6]\n", + "\n", + "def age(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Age function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[:, 7]\n", + "\n", + "def halfmassrad_stars(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Halfmassrad_stars function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[:, 8]\n", + "\n", + "def redshift(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Redshift function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[:, 9]\n", + "\n", + "def center(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Center function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[:, 10:13]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.nbytes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "try the sharding now. this involves the build up of the pipeline from the ground up in such a way that the data is sharded once and then we don´t have to touch it again" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# original system" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "rubixdata = pipe.run_sharded(inputdata)" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-28 12:10:11,524 - rubix - INFO - Getting rubix data...\n", - "2025-04-28 12:10:11,526 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-04-28 12:10:11,563 - rubix - INFO - Centering stars particles\n", - "2025-04-28 12:10:11,892 - rubix - WARNING - The Subset value is set in config. Using only subset of size 30000 for stars\n", - "2025-04-28 12:10:11,894 - rubix - INFO - Data loaded with 30000 star particles and 0 gas particles.\n", - "2025-04-28 12:10:11,895 - rubix - INFO - Setting up the pipeline...\n", - "2025-04-28 12:10:11,895 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-04-28 12:10:11,896 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-04-28 12:10:11,897 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:10:11,914 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:10:11,935 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:10:11,954 - rubix - INFO - Getting cosmology...\n", - "2025-04-28 12:10:12,038 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-04-28 12:10:12,094 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:10:12,175 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:10:12,197 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:10:12,235 - rubix - INFO - Assembling the pipeline...\n", - "2025-04-28 12:10:12,236 - rubix - INFO - Compiling the expressions...\n", - "2025-04-28 12:10:12,237 - rubix - INFO - Number of devices: 3\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-04-28 12:10:12,850 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-04-28 12:10:12,947 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-04-28 12:10:12,951 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-04-28 12:10:12,954 - rubix - INFO - Calculating IFU cube...\n", - "2025-04-28 12:10:12,954 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-04-28 12:10:12,955 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-04-28 12:10:12,956 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-04-28 12:10:12,959 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-04-28 12:10:12,960 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-04-28 12:10:12,960 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-04-28 12:10:12,967 - rubix - INFO - Calculating Data Cube...\n", - "2025-04-28 12:10:12,969 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-04-28 12:10:12,969 - rubix - INFO - Convolving with PSF...\n", - "2025-04-28 12:10:12,972 - rubix - INFO - Convolving with LSF...\n", - "2025-04-28 12:10:12,976 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-04-28 12:12:57,367 - rubix - INFO - Pipeline run completed in 165.47 seconds.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -444,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -465,20 +660,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -514,20 +698,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", @@ -568,7 +741,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rubix", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -582,7 +755,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/rubix/core/data.py b/rubix/core/data.py index 4c27cc90..d00478f0 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -1,22 +1,20 @@ +import logging import os -from typing import Callable, Union, Optional from dataclasses import dataclass from functools import partial +from typing import Callable, Optional, Union import jax import jax.numpy as jnp import numpy as np +from beartype import beartype as typechecker +from jaxtyping import jaxtyped from rubix.galaxy import IllustrisAPI, get_input_handler from rubix.galaxy.alignment import center_particles from rubix.logger import get_logger from rubix.utils import load_galaxy_data, read_yaml -import logging -from jaxtyping import jaxtyped -from beartype import beartype as typechecker - - # class Particles: # def __init__(self, particle_data: object): # self.particle_data = particle_data @@ -64,7 +62,7 @@ # Registering the dataclass with JAX for automatic tree traversal -#@jaxtyped(typechecker=typechecker) +# @jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class Galaxy: @@ -81,7 +79,7 @@ class Galaxy: center: Optional[jnp.ndarray] = None halfmassrad_stars: Optional[jnp.ndarray] = None - #def __repr__(self): + # def __repr__(self): # representationString = ["Galaxy:"] # for k, v in self.__dict__.items(): # if not k.endswith("_unit"): @@ -122,7 +120,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -#@jaxtyped(typechecker=typechecker) +# @jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class StarsData: @@ -154,7 +152,7 @@ class StarsData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - #def __repr__(self): + # def __repr__(self): # representationString = ["StarsData:"] # for k, v in self.__dict__.items(): # if not k.endswith("_unit"): @@ -206,7 +204,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -#@jaxtyped(typechecker=typechecker) +# @jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class GasData: @@ -244,7 +242,7 @@ class GasData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - #def __repr__(self): + # def __repr__(self): # representationString = ["GasData:"] # for k, v in self.__dict__.items(): # if not k.endswith("_unit"): @@ -300,7 +298,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -#@jaxtyped(typechecker=typechecker) +# @jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class RubixData: @@ -317,7 +315,7 @@ class RubixData: stars: Optional[StarsData] = None gas: Optional[GasData] = None - #def __repr__(self): + # def __repr__(self): # representationString = ["RubixData:"] # for k, v in self.__dict__.items(): # representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) @@ -434,6 +432,8 @@ def convert_to_rubix(config: Union[dict, str]): logger.info("Loading data from IllustrisAPI") api = IllustrisAPI(**config["data"]["args"], logger=logger) api.load_galaxy(**config["data"]["load_galaxy_args"]) + else: + raise ValueError(f"Unknown data source: {config['data']['name']}.") # Load the saved data into the input handler logger.info("Loading data into input handler") From ef8c0697bdaeb87582c8d32eb27fc3d59aca3dfe Mon Sep 17 00:00:00 2001 From: Harald Mack Date: Mon, 5 May 2025 17:50:07 +0200 Subject: [PATCH 16/76] work on sharding test pipeline in separate notebook --- notebooks/pipeline_sharding_test.ipynb | 1067 ++++++++++++++++++++++++ pyproject.toml | 11 +- rubix/pipeline/linear_pipeline.py | 7 +- 3 files changed, 1074 insertions(+), 11 deletions(-) create mode 100644 notebooks/pipeline_sharding_test.ipynb diff --git a/notebooks/pipeline_sharding_test.ipynb b/notebooks/pipeline_sharding_test.ipynb new file mode 100644 index 00000000..f03b2a48 --- /dev/null +++ b/notebooks/pipeline_sharding_test.ipynb @@ -0,0 +1,1067 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import multiprocessing\n", + "\n", + "# Logical cores (includes hyperthreads)\n", + "print(\"Logical cores:\", os.cpu_count())\n", + "\n", + "\n", + "# Total threads/cores via multiprocessing\n", + "print(\"multiprocessing.cpu_count():\", multiprocessing.cpu_count())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "\n", + "# Tell XLA to fake 2 host CPU devices\n", + "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", + "\n", + "# Only make GPU 0 and GPU 1 visible to JAX:\n", + "# os.environ['CUDA_VISIBLE_DEVICES'] = '7, 8, 9'\n", + "\n", + "# for making sure that JAX doesnt'consume all memory at once\n", + "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", + "\n", + "import jax\n", + "# Now JAX will list two CpuDevice entries\n", + "print(jax.devices())\n", + "# → [CpuDevice(id=0), CpuDevice(id=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "os.environ['SPS_HOME'] = '/home/hmack/.cache/fsps'\n", + "os.environ['ILLUSTRIS_API_KEY'] = ''" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "# RUBIX pipeline\n", + "\n", + "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execute the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline on multiple machines. To see, how the pipeline is executed in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", + "\n", + "## How to use the Pipeline\n", + "1) Define a `config`\n", + "2) Setup the `pipeline yaml`\n", + "3) Run the RUBIX pipeline\n", + "4) Do science with the mock-data" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "## Step 1: Config\n", + "\n", + "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", + "\n", + "For the `config` you can choose the following options:\n", + "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", + "- `logger`: RUBIX has implemented a logger to report to the user, what is happening during the pipeline execution and give warnings\n", + "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", + "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", + "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", + "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", + "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", + "- `data - load_galaxy_args - reuse`: if True, if in the save_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", + "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", + "- `simulation - name`: currently only IllustrisTNG is supported\n", + "- `simulation - args - path`: where the data is stored and how the file will be named\n", + "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", + "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", + "- `telescope - psf`: define the point spread function that is applied to the mock data\n", + "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", + "- `telescope - noise`: define the noise that is applied to the mock data\n", + "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", + "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", + "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", + "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "\n", + "config = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 14,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 30000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-14.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"FSPS\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "## Step 2: Pipeline yaml\n", + "\n", + "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", + "\n", + "```yaml\n", + "calc_ifu:\n", + " Transformers:\n", + " rotate_galaxy:\n", + " name: rotate_galaxy\n", + " depends_on: null\n", + " args: []\n", + " kwargs:\n", + " type: \"face-on\"\n", + " filter_particles:\n", + " name: filter_particles\n", + " depends_on: rotate_galaxy\n", + " args: []\n", + " kwargs: {}\n", + " spaxel_assignment:\n", + " name: spaxel_assignment\n", + " depends_on: filter_particles\n", + " args: []\n", + " kwargs: {}\n", + " reshape_data:\n", + " name: reshape_data\n", + " depends_on: spaxel_assignment\n", + " args: []\n", + " kwargs: {}\n", + " calculate_spectra:\n", + " name: calculate_spectra\n", + " depends_on: reshape_data\n", + " args: []\n", + " kwargs: {}\n", + " scale_spectrum_by_mass:\n", + " name: scale_spectrum_by_mass\n", + " depends_on: calculate_spectra\n", + " args: []\n", + " kwargs: {}\n", + " doppler_shift_and_resampling:\n", + " name: doppler_shift_and_resampling\n", + " depends_on: scale_spectrum_by_mass\n", + " args: []\n", + " kwargs: {}\n", + " calculate_datacube:\n", + " name: calculate_datacube\n", + " depends_on: doppler_shift_and_resampling\n", + " args: []\n", + " kwargs: {}\n", + " convolve_psf:\n", + " name: convolve_psf\n", + " depends_on: calculate_datacube\n", + " args: []\n", + " kwargs: {}\n", + " convolve_lsf:\n", + " name: convolve_lsf\n", + " depends_on: convolve_psf\n", + " args: []\n", + " kwargs: {}\n", + " apply_noise:\n", + " name: apply_noise\n", + " depends_on: convolve_lsf\n", + " args: []\n", + " kwargs: {}\n", + "```\n", + "\n", + "There is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config)\n", + "inputdata = pipe.prepare_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "from jax.sharding import PartitionSpec as P, NamedSharding\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + " \n", + "mesh = jax.make_mesh((jax.device_count(), ), ('x',))\n", + "shard = NamedSharding(mesh, P('x'))\n", + "data = jax.device_put(inputdata, shard)" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "why this no work?? " + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "try simpler approach for this thing for now. This is really stupid: just build a giant box of zeros, index into them in the right way, and use these indices to assign the values we want to slices in the box" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "# this function builds the data from the rubixdata object because that is easiest, but should not really be done imho. \n", + "def build_data(input): \n", + " long_axis = input.stars.age.shape[0]\n", + " data = jnp.zeros((long_axis, 6200), dtype=jnp.float32)\n", + " inputdata.galaxy.redshift = jnp.float32(inputdata.galaxy.redshift)\n", + " inputdata.galaxy.halfmassrad_stars = jnp.array(inputdata.galaxy.halfmassrad_stars, dtype=jnp.float32)\n", + " inputdata.galaxy.center = jnp.array(inputdata.galaxy.center, dtype=jnp.float32)\n", + "\n", + " inputdata.stars.coords = jnp.array(inputdata.stars.coords, dtype=jnp.float32)\n", + " inputdata.stars.age = jnp.array(inputdata.stars.age, dtype=jnp.float32)\n", + " inputdata.stars.velocity = jnp.array(inputdata.stars.velocity, dtype=jnp.float32)\n", + " inputdata.stars.metallicity = jnp.array(inputdata.stars.metallicity, dtype=jnp.float32)\n", + " inputdata.stars.mass = jnp.array(inputdata.stars.mass, dtype=jnp.float32)\n", + " # stars properties\n", + " data = data.at[:, 0:3].set(inputdata.stars.coords)\n", + " data = data.at[:, 3:6].set(inputdata.stars.velocity)\n", + " data = data.at[:, 6].set(inputdata.stars.metallicity)\n", + " data = data.at[:, 7].set(inputdata.stars.age)\n", + " data = data.at[:, 8].set(inputdata.stars.mass)\n", + "\n", + " # galaxy properties\n", + " data = data.at[:, 9].set(inputdata.galaxy.halfmassrad_stars)\n", + " data = data.at[:, 10].set(inputdata.galaxy.redshift)\n", + " data = data.at[:, 11:14].set(inputdata.galaxy.center)\n", + " \n", + " mesh = jax.make_mesh((jax.device_count(), ), ('x',))\n", + " shard = NamedSharding(mesh, P('x'))\n", + "\n", + " data = jax.device_put(data, shard)\n", + "\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "def stars(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Stars function to be used in the pipeline.\n", + " \"\"\"\n", + " # Perform some operations on the data\n", + " # For example, let's just return the data as is\n", + " return data[:, 0:9]\n", + "\n", + "def galaxy(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Galaxy function to be used in the pipeline.\n", + " \"\"\"\n", + " # Perform some operations on the data\n", + " # For example, let's just return the data as is\n", + " return data[:, 9:14]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def coords_idx(): \n", + " return jnp.s_[:, 0:3]\n", + "\n", + "def coords(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Coords function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[coords_idx()]\n", + "\n", + "def velocity_idx():\n", + " return jnp.s_[:, 3:6]\n", + "\n", + "def velocity(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Velocity function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[velocity_idx()]\n", + "\n", + "def metallicity_idx():\n", + " return jnp.s_[:, 6]\n", + "\n", + "def metallicity(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Metallicity function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[metallicity_idx()]\n", + "\n", + "def age_idx():\n", + " return jnp.s_[:, 7]\n", + "\n", + "def age(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Age function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[age_idx()]\n", + "\n", + "def mass_idx():\n", + " return jnp.s_[:, 8]\n", + "\n", + "def mass(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Age function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[mass_idx()]\n", + "\n", + "def halfmassrad_stars_idx():\n", + " return jnp.s_[:, 9]\n", + "\n", + "def halfmassrad_stars(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Halfmassrad_stars function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[halfmassrad_stars_idx()]\n", + "\n", + "\n", + "def redshift_idx():\n", + " return jnp.s_[:, 10]\n", + "\n", + "def redshift(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Redshift function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[redshift_idx()]\n", + "\n", + "def center_idx():\n", + " return jnp.s_[:, 11:14]\n", + "\n", + "def center(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Center function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[center_idx()]\n", + "\n", + "def mask_idx() :\n", + " return jnp.s_[:, 14]\n", + "\n", + "def mask(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Mask function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[mask_idx()]\n", + "\n", + "def pixel_assignment_idx() : \n", + " return jnp.s_[:, 15]\n", + "\n", + "def pixel_assignment(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Pixel assignment function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[pixel_assignment_idx()]\n", + "\n", + "\n", + "def spectra_index(): \n", + " return jnp.s_[:, 16:(16 + 5994)]\n", + "\n", + "def spectra(data: jnp.ndarray) -> jnp.ndarray:\n", + " \"\"\"\n", + " Spectra function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[spectra_index()]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [ + "data = build_data(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "data.nbytes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "jax.debug.visualize_array_sharding(data)\n" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "try the sharding now with pipeline functions. since the pipeline functions use other data, I don´t use them directly, but build simplified versions here that only include stars. this involves the build up of the pipeline from the ground up in such a way that the data is sharded once and then we don´t have to touch it again" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": {}, + "source": [ + "TODO: make sure the functions have the correct static argnums such that we don´t have to worry about the tracing shit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "from pipe import Pipe" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "metadata": {}, + "source": [ + "galaxy rotation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [], + "source": [ + "from rubix.galaxy.alignment import moment_of_inertia_tensor, rotation_matrix_from_inertia_tensor, apply_init_rotation, apply_rotation\n", + "\n", + "def rotate_galaxy_impl(data: jnp.array, alpha, beta, gamma)->jnp.array: \n", + "\n", + " I = moment_of_inertia_tensor(coords(data), mass(data), halfmassrad_stars(data),)\n", + " R = rotation_matrix_from_inertia_tensor(I)\n", + " data = data.at[coords_idx()].set(apply_rotation(apply_init_rotation(coords(data), R), alpha, beta, gamma))\n", + " data = data.at[velocity_idx()].set(apply_rotation(apply_init_rotation(velocity(data), R), alpha, beta, gamma))\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": {}, + "outputs": [], + "source": [ + "r = rotate_galaxy_impl(data, 0.1, 0.2, 0.3)\n", + "type(r), r.shape, r.dtype, r.nbytes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [], + "source": [ + "rotate_galaxy = partial(rotate_galaxy_impl, alpha=90.0, beta=0.0, gamma=0.0)" + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": {}, + "source": [ + "filter particles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27", + "metadata": {}, + "outputs": [], + "source": [ + "from rubix.core.telescope import get_spatial_bin_edges\n", + "from rubix.telescope.utils import mask_particles_outside_aperture\n", + "\n", + "\n", + "def filter_particles_impl(data: jnp.ndarray, spatial_bin_edges) -> jnp.ndarray:\n", + " mask = mask_particles_outside_aperture(\n", + " coords(data), spatial_bin_edges\n", + " )\n", + "\n", + " data = data.at[mask_idx()].set(mask)\n", + "\n", + " for attr in [age_idx, mass_idx, metallicity_idx, ]: \n", + " data = data.at[attr()].set(\n", + " jnp.where(mask, data[attr()], 0)\n", + " )\n", + "\n", + " return data\n", + "\n", + "filter_particles = partial(filter_particles_impl, spatial_bin_edges=get_spatial_bin_edges(config))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28", + "metadata": {}, + "outputs": [], + "source": [ + "get_spatial_bin_edges(config).shape " + ] + }, + { + "cell_type": "markdown", + "id": "29", + "metadata": {}, + "source": [ + "try it out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30", + "metadata": {}, + "outputs": [], + "source": [ + "data = filter_particles(data)" + ] + }, + { + "cell_type": "markdown", + "id": "31", + "metadata": {}, + "source": [ + "try out simple pipeline " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32", + "metadata": {}, + "outputs": [], + "source": [ + "data = inputdata | Pipe(build_data) | Pipe(rotate_galaxy) | Pipe(filter_particles)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33", + "metadata": {}, + "outputs": [], + "source": [ + "data.shape, data.nbytes / 1024**2, data.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34", + "metadata": {}, + "outputs": [], + "source": [ + "jax.debug.visualize_array_sharding(data)" + ] + }, + { + "cell_type": "markdown", + "id": "35", + "metadata": {}, + "source": [ + "try to compile it and run it then,then check sharding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36", + "metadata": {}, + "outputs": [], + "source": [ + "from rubix.core.pipeline import RubixPipeline \n", + "from rubix.core.data import RubixData" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37", + "metadata": {}, + "outputs": [], + "source": [ + "@jax.jit \n", + "def pipeline(data: jnp.array) -> jnp.ndarray:\n", + " data = rotate_galaxy(data)\n", + " data = filter_particles(data)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38", + "metadata": {}, + "outputs": [], + "source": [ + "data = build_data(inputdata)\n", + "data = pipeline(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39", + "metadata": {}, + "outputs": [], + "source": [ + "data.shape, data.nbytes / 1024**2, data.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40", + "metadata": {}, + "outputs": [], + "source": [ + "jax.debug.visualize_array_sharding(data)" + ] + }, + { + "cell_type": "markdown", + "id": "41", + "metadata": {}, + "source": [ + "spaxel assignment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42", + "metadata": {}, + "outputs": [], + "source": [ + "def spaxel_assignment_square_impl(data: jnp.ndarray, spatial_bin_edges)-> jnp.ndarray:\n", + " # Calculate assignment of of x and y coordinates to bins separately\n", + " x_indices = (\n", + " jnp.digitize(data[coords_idx()][:, 0], spatial_bin_edges) - 1\n", + " ) # -1 to start indexing at 0\n", + " y_indices = jnp.digitize(data[coords_idx()][:, 1], spatial_bin_edges) - 1\n", + "\n", + " number_of_bins = len(spatial_bin_edges) - 1\n", + "\n", + " # Clip the indices to the valid range\n", + " x_indices = jnp.clip(x_indices, 0, number_of_bins - 1)\n", + " y_indices = jnp.clip(y_indices, 0, number_of_bins - 1)\n", + "\n", + " # Flatten the 2D indices to 1D indices\n", + " pixel_positions = x_indices + (number_of_bins * y_indices)\n", + " return data.at[pixel_assignment_idx()].set(jnp.round(pixel_positions))\n", + "\n", + "\n", + "spaxel_assignment = partial(spaxel_assignment_square_impl, spatial_bin_edges=get_spatial_bin_edges(config))\n" + ] + }, + { + "cell_type": "markdown", + "id": "43", + "metadata": {}, + "source": [ + "try it out again" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44", + "metadata": {}, + "outputs": [], + "source": [ + "data = inputdata | Pipe(build_data) | Pipe(rotate_galaxy) | Pipe(filter_particles) | Pipe(spaxel_assignment)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45", + "metadata": {}, + "outputs": [], + "source": [ + "jax.debug.visualize_array_sharding(data)" + ] + }, + { + "cell_type": "markdown", + "id": "46", + "metadata": {}, + "source": [ + "calculate spectra now. since this is so big, it would perpaps make sense to have a separate path for this thing instead of having to save this and drag it around all the time. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47", + "metadata": {}, + "outputs": [], + "source": [ + "from rubix.core.ssp import get_ssp, get_lookup_interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_spectra_impl(data: jnp.ndarray, lookup_interpolation) -> jnp.ndarray: \n", + "\n", + " # this thing is gigantic and probably cannot be stored in memory for serious data\n", + " return data.at[spectra_index()].set(lookup_interpolation(\n", + " data[metallicity_idx()],\n", + " data[age_idx()],\n", + " ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49", + "metadata": {}, + "outputs": [], + "source": [ + "lookup_interpolation = get_lookup_interpolation(config)\n", + "\n", + "calculate_spectra = partial(calculate_spectra_impl, lookup_interpolation=lookup_interpolation)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50", + "metadata": {}, + "outputs": [], + "source": [ + "data = inputdata | Pipe(build_data) | Pipe(rotate_galaxy) | Pipe(filter_particles) | Pipe(spaxel_assignment) | Pipe(calculate_spectra)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51", + "metadata": {}, + "outputs": [], + "source": [ + "type(data), data.shape, data.dtype, data.nbytes / 1024**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52", + "metadata": {}, + "outputs": [], + "source": [ + "jax.debug.visualize_array_sharding(data)" + ] + }, + { + "cell_type": "markdown", + "id": "53", + "metadata": {}, + "source": [ + "scale spectrum by mass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54", + "metadata": {}, + "outputs": [], + "source": [ + "def scale_spectrum_by_mass(data: jnp.ndarray) -> jnp.ndarray:\n", + "\n", + " return data.at[spectra_index()].set(\n", + " data[spectra_index()] * data[mass_idx()][:, jnp.newaxis]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55", + "metadata": {}, + "outputs": [], + "source": [ + "data = scale_spectrum_by_mass(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56", + "metadata": {}, + "outputs": [], + "source": [ + "type(data), data.shape, data.dtype, data.nbytes / 1024**2" + ] + }, + { + "cell_type": "markdown", + "id": "57", + "metadata": {}, + "source": [ + "So far, we barely need 710 MB for everything we do, and we are not efficient at all wrt memory. On multiple GPUs with overall 100GB, we should easily be able to process the required data sizes? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58", + "metadata": {}, + "outputs": [], + "source": [ + "jax.debug.visualize_array_sharding(data)" + ] + }, + { + "cell_type": "markdown", + "id": "59", + "metadata": {}, + "source": [ + "doppler shift" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60", + "metadata": {}, + "outputs": [], + "source": [ + "# get all the needed crap... \n", + "from rubix import config as rubix_config\n", + "velocity_direction = rubix_config[\"ifu\"][\"doppler\"][\"velocity_direction\"]\n", + "directions = {\"x\": 0, \"y\": 1, \"z\": 2}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61", + "metadata": {}, + "outputs": [], + "source": [ + "velocity_direction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62", + "metadata": {}, + "outputs": [], + "source": [ + "def apply_doppler_impl(data: jnp.ndarray, wavelength, c, direction) -> jnp.ndarray:\n", + " print(\"shapes: \", data[velocity_idx()].shape, wavelength.shape)\n", + "\n", + " # FIXME: this needs to be vmapped or broadcasted in such a way that every velocity component is doppler shifted for each wavelength. \n", + " # calculate classic doppler shift \n", + " v = data[velocity_idx()][:, direction]\n", + " return data.at[velocity_idx()][:, direction].set(\n", + " wavelength * jnp.exp(v/c)\n", + " )\n", + "\n", + "ssp = get_ssp(config)\n", + "ssp_wave= ssp.wavelength\n", + "direction = directions[velocity_direction]\n", + "cosmological_doppler_shift = (1 + config[\"galaxy\"][\"dist_z\"]) * ssp.wavelength\n", + "\n", + "apply_doppler = partial(apply_doppler_impl, wavelength=ssp_wave, c=3e8, direction=direction)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63", + "metadata": {}, + "outputs": [], + "source": [ + "apply_doppler(data)" + ] + }, + { + "cell_type": "markdown", + "id": "64", + "metadata": {}, + "source": [ + "resampling" + ] + }, + { + "cell_type": "markdown", + "id": "65", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 24a63268..fa512b63 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,9 +32,7 @@ dependencies = [ "pyaml", "jaxtyping", "equinox", - "jax[cpu]!=0.4.27", - "jax[cpu]!=0.4.36", - "jax[cpu]!=0.5.1", + "jax[cpu]>0.5.1", "interpax", "astroquery", "beartype", @@ -51,8 +49,7 @@ tests = [ "pytest-cov", "pytest-mock", "nbval", - "jax[cpu]!=0.4.27", - "jax[cpu]!=0.4.36", + "jax[cpu]>0.5.1", "pre-commit", ] docs = [ @@ -63,10 +60,8 @@ docs = [ "sphinx_mdinclude", "sphinx_rtd_theme", ] - cuda = [ - "jax[cuda]!=0.4.27", - "jax[cuda]!=0.4.36", + "jax[cuda]>0.5.1", ] diff --git a/rubix/pipeline/linear_pipeline.py b/rubix/pipeline/linear_pipeline.py index 8d270f75..bc6de13e 100644 --- a/rubix/pipeline/linear_pipeline.py +++ b/rubix/pipeline/linear_pipeline.py @@ -1,7 +1,9 @@ +from copy import deepcopy + +from jax.tree_util import Partial + from . import abstract_pipeline as apl from .transformer import bound_transformer -from jax.tree_util import Partial -from copy import deepcopy class LinearTransformerPipeline(apl.AbstractPipeline): @@ -176,7 +178,6 @@ def apply(self, *args, static_args=[], static_kwargs=[], **kwargs): ValueError _description_ """ - print("Arguments: ", *args) if len(args) == 0: raise ValueError("Cannot apply the pipeline to an empty list of arguments") From 439659064a27ac3544229f6fda4d238cd416f111 Mon Sep 17 00:00:00 2001 From: Harald Mack Date: Mon, 5 May 2025 17:52:31 +0200 Subject: [PATCH 17/76] reset single function notebook --- ...x_pipeline_single_function_shard_map.ipynb | 361 +----------------- 1 file changed, 13 insertions(+), 348 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index fa82cf1a..54d6e814 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -1,14 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import jax.numpy as jnp" - ] - }, { "cell_type": "code", "execution_count": null, @@ -38,9 +29,8 @@ "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "# os.environ['CUDA_VISIBLE_DEVICES'] = '7, 8, 9'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '7, 8, 9'\n", "\n", - "# for making sure that JAX doesnt'consume all memory at once\n", "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", "import jax\n", @@ -61,8 +51,7 @@ "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "os.environ['SPS_HOME'] = '/home/hmack/.cache/fsps'\n", - "os.environ['ILLUSTRIS_API_KEY'] = '17c621e8278dcedebc7099b0861105c2'" + "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" ] }, { @@ -71,7 +60,7 @@ "source": [ "# RUBIX pipeline\n", "\n", - "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execute the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline on multiple machines. To see, how the pipeline is executed in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", + "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", "\n", "## How to use the Pipeline\n", "1) Define a `config`\n", @@ -90,13 +79,13 @@ "\n", "For the `config` you can choose the following options:\n", "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", - "- `logger`: RUBIX has implemented a logger to report to the user, what is happening during the pipeline execution and give warnings\n", + "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", - "- `data - load_galaxy_args - reuse`: if True, if in the save_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", + "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", "- `simulation - name`: currently only IllustrisTNG is supported\n", "- `simulation - args - path`: where the data is stored and how the file will be named\n", @@ -121,7 +110,6 @@ "import matplotlib.pyplot as plt\n", "from rubix.core.pipeline import RubixPipeline \n", "import os\n", - "\n", "config = {\n", " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", @@ -171,6 +159,7 @@ " {\"dist_z\": 0.1,\n", " \"rotation\": {\"type\": \"edge-on\"},\n", " },\n", + " \n", " \"ssp\": {\n", " \"template\": {\n", " \"name\": \"FSPS\"\n", @@ -213,16 +202,19 @@ " depends_on: filter_particles\n", " args: []\n", " kwargs: {}\n", + "\n", " reshape_data:\n", " name: reshape_data\n", " depends_on: spaxel_assignment\n", " args: []\n", " kwargs: {}\n", + "\n", " calculate_spectra:\n", " name: calculate_spectra\n", " depends_on: reshape_data\n", " args: []\n", " kwargs: {}\n", + "\n", " scale_spectrum_by_mass:\n", " name: scale_spectrum_by_mass\n", " depends_on: calculate_spectra\n", @@ -255,7 +247,7 @@ " kwargs: {}\n", "```\n", "\n", - "There is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" + "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" ] }, { @@ -285,334 +277,7 @@ "source": [ "#NBVAL_SKIP\n", "\n", - "inputdata = pipe.prepare_data()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.galaxy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "there is a type instability here, and this number is also massively too low." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.galaxy.redshift, type(inputdata.galaxy.redshift), inputdata.galaxy.redshift.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.galaxy.center, type(inputdata.galaxy.center), inputdata.galaxy.center.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.galaxy.halfmassrad_stars, type(inputdata.galaxy.halfmassrad_stars), inputdata.galaxy.halfmassrad_stars.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.stars.coords, type(inputdata.stars.coords), inputdata.stars.coords.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.stars.velocity, type(inputdata.stars.velocity), inputdata.stars.velocity.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.stars.metallicity, type(inputdata.stars.metallicity), inputdata.stars.metallicity.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.stars.spectra, type(inputdata.stars.spectra), inputdata.stars.spectra.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.stars.mask, type(inputdata.stars.mask), inputdata.stars.mask.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.stars.pixel_assignment, type(inputdata.stars.pixel_assignment), inputdata.stars.pixel_assignment.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.stars.age, type(inputdata.stars.age), inputdata.stars.age.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.gas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.gas.coords, type(inputdata.gas.coords), inputdata.gas.coords.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.galaxy.redshift = jnp.float32(inputdata.galaxy.redshift)\n", - "type(inputdata.galaxy.redshift)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The below needs to be made part of the data system itself, we don't want this to ever land in numpy. It also needs to be collated and put into a somewhat useful shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.galaxy.redshift = jnp.float32(inputdata.galaxy.redshift)\n", - "inputdata.galaxy.halfmassrad_stars = jnp.array(inputdata.galaxy.halfmassrad_stars, dtype=jnp.float32)\n", - "inputdata.galaxy.center = jnp.array(inputdata.galaxy.center, dtype=jnp.float32)\n", - "\n", - "inputdata.stars.coords = jnp.array(inputdata.stars.coords, dtype=jnp.float32)\n", - "inputdata.stars.age = jnp.array(inputdata.stars.age, dtype=jnp.float32)\n", - "inputdata.stars.velocity = jnp.array(inputdata.stars.velocity, dtype=jnp.float32)\n", - "inputdata.stars.metallicity = jnp.array(inputdata.stars.metallicity, dtype=jnp.float32)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inputdata.galaxy.center" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = jnp.zeros((30000, 13), dtype=jnp.float32)\n", - "\n", - "# stars properties\n", - "data = data.at[:, 0:3].set(inputdata.stars.coords)\n", - "data = data.at[:, 3:6].set(inputdata.stars.velocity)\n", - "data = data.at[:, 6].set(inputdata.stars.metallicity)\n", - "data = data.at[:, 7].set(inputdata.stars.age)\n", - "\n", - "# galaxy properties\n", - "data = data.at[:, 8].set(inputdata.galaxy.halfmassrad_stars)\n", - "data = data.at[:, 9].set(inputdata.galaxy.redshift)\n", - "data = data.at[:, 10:13].set(inputdata.galaxy.center)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def stars(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Stars function to be used in the pipeline.\n", - " \"\"\"\n", - " # Perform some operations on the data\n", - " # For example, let's just return the data as is\n", - " return data[:, 0:8]\n", - "\n", - "def galaxy(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Galaxy function to be used in the pipeline.\n", - " \"\"\"\n", - " # Perform some operations on the data\n", - " # For example, let's just return the data as is\n", - " return data[:, 8:13]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def coords(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Coords function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[:, 0:3]\n", - "\n", - "def velocity(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Velocity function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[:, 3:6]\n", - "\n", - "def metallicity(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Metallicity function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[:, 6]\n", - "\n", - "def age(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Age function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[:, 7]\n", - "\n", - "def halfmassrad_stars(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Halfmassrad_stars function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[:, 8]\n", - "\n", - "def redshift(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Redshift function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[:, 9]\n", - "\n", - "def center(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Center function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[:, 10:13]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.nbytes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "try the sharding now. this involves the build up of the pipeline from the ground up in such a way that the data is sharded once and then we don´t have to touch it again" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# original system" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "inputdata = pipe.prepare_data()\n", "rubixdata = pipe.run_sharded(inputdata)" ] }, @@ -741,7 +406,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "rubix", "language": "python", "name": "python3" }, @@ -755,7 +420,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.9" } }, "nbformat": 4, From 1cadba4bd904eddc22e0cbc61d5c872768ec9308 Mon Sep 17 00:00:00 2001 From: Harald Mack Date: Tue, 6 May 2025 15:59:05 +0200 Subject: [PATCH 18/76] finish test notebook --- .gitignore | 4 + notebooks/pipeline_sharding_test.ipynb | 990 ++++++++++++++++++------- 2 files changed, 719 insertions(+), 275 deletions(-) diff --git a/.gitignore b/.gitignore index 33b4ede5..3b11d146 100644 --- a/.gitignore +++ b/.gitignore @@ -174,3 +174,7 @@ rubix/spectra/ssp/templates/fsps.h5 notebooks/frames notebooks/frames/* notebooks/nohup.out + + +# don´t add .env files +*.env diff --git a/notebooks/pipeline_sharding_test.ipynb b/notebooks/pipeline_sharding_test.ipynb index f03b2a48..d12f2654 100644 --- a/notebooks/pipeline_sharding_test.ipynb +++ b/notebooks/pipeline_sharding_test.ipynb @@ -7,7 +7,12 @@ "metadata": {}, "outputs": [], "source": [ - "import jax.numpy as jnp" + "# NBVAL_SKIP\n", + "\n", + "\n", + "import os\n", + "import multiprocessing\n", + "import matplotlib.pyplot as plt\n" ] }, { @@ -17,8 +22,7 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import multiprocessing\n", + "# NBVAL_SKIP\n", "\n", "# Logical cores (includes hyperthreads)\n", "print(\"Logical cores:\", os.cpu_count())\n", @@ -35,22 +39,18 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", + "# use dotenv to handle env variables\n", "import os\n", + "from dotenv import load_dotenv\n", + "env_loaded =load_dotenv(dotenv_path='./data.env')\n", + "assert env_loaded, \"Failed to load .env file\"\n", "\n", - "\n", - "# Tell XLA to fake 2 host CPU devices\n", - "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", - "\n", - "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "# os.environ['CUDA_VISIBLE_DEVICES'] = '7, 8, 9'\n", - "\n", - "# for making sure that JAX doesnt'consume all memory at once\n", - "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", - "\n", + "import jax.numpy as jnp\n", "import jax\n", - "# Now JAX will list two CpuDevice entries\n", - "print(jax.devices())\n", - "# → [CpuDevice(id=0), CpuDevice(id=1)]" + "from jax.sharding import PartitionSpec as P, NamedSharding\n", + "\n", + "from rubix.core.pipeline import RubixPipeline \n" ] }, { @@ -61,12 +61,8 @@ "outputs": [], "source": [ "# NBVAL_SKIP\n", - "import os\n", - "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "os.environ['SPS_HOME'] = '/home/hmack/.cache/fsps'\n", - "os.environ['ILLUSTRIS_API_KEY'] = ''" + "\n", + "print(jax.devices())\n" ] }, { @@ -125,9 +121,7 @@ "outputs": [], "source": [ "#NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "from rubix.core.pipeline import RubixPipeline \n", - "import os\n", + "\n", "\n", "config = {\n", " \"pipeline\":{\"name\": \"calc_ifu\"},\n", @@ -267,25 +261,19 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "8", "metadata": {}, - "outputs": [], "source": [ - "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config)\n", - "inputdata = pipe.prepare_data()" + "# Data organization" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "9", "metadata": {}, - "outputs": [], "source": [ - "from jax.sharding import PartitionSpec as P, NamedSharding\n" + "try simple approach for this thing for now. This is really stupid: just build a giant box of zeros, index into them in the right way, and use these indices to assign the values we want to slices in the box" ] }, { @@ -295,38 +283,12 @@ "metadata": {}, "outputs": [], "source": [ - " \n", - "mesh = jax.make_mesh((jax.device_count(), ), ('x',))\n", - "shard = NamedSharding(mesh, P('x'))\n", - "data = jax.device_put(inputdata, shard)" - ] - }, - { - "cell_type": "markdown", - "id": "11", - "metadata": {}, - "source": [ - "why this no work?? " - ] - }, - { - "cell_type": "markdown", - "id": "12", - "metadata": {}, - "source": [ - "try simpler approach for this thing for now. This is really stupid: just build a giant box of zeros, index into them in the right way, and use these indices to assign the values we want to slices in the box" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13", - "metadata": {}, - "outputs": [], - "source": [ + "# NBVAL_SKIP\n", + "\n", + "\n", "# this function builds the data from the rubixdata object because that is easiest, but should not really be done imho. \n", - "def build_data(input): \n", - " long_axis = input.stars.age.shape[0]\n", + "def build_data(inputdata): \n", + " long_axis = inputdata.stars.age.shape[0]\n", " data = jnp.zeros((long_axis, 6200), dtype=jnp.float32)\n", " inputdata.galaxy.redshift = jnp.float32(inputdata.galaxy.redshift)\n", " inputdata.galaxy.halfmassrad_stars = jnp.array(inputdata.galaxy.halfmassrad_stars, dtype=jnp.float32)\n", @@ -360,10 +322,12 @@ { "cell_type": "code", "execution_count": null, - "id": "14", + "id": "11", "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", + "\n", "def stars(data: jnp.ndarray) -> jnp.ndarray:\n", " \"\"\"\n", " Stars function to be used in the pipeline.\n", @@ -372,6 +336,9 @@ " # For example, let's just return the data as is\n", " return data[:, 0:9]\n", "\n", + "def gas(data: jnp.ndarray) -> jnp.ndarray:\n", + " return data # index after adjusting the above for gas\n", + "\n", "def galaxy(data: jnp.ndarray) -> jnp.ndarray:\n", " \"\"\"\n", " Galaxy function to be used in the pipeline.\n", @@ -384,11 +351,11 @@ { "cell_type": "code", "execution_count": null, - "id": "15", + "id": "12", "metadata": {}, "outputs": [], "source": [ - "\n", + "# NBVAL_SKIP\n", "\n", "def coords_idx(): \n", " return jnp.s_[:, 0:3]\n", @@ -492,39 +459,9 @@ " return data[spectra_index()]\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "16", - "metadata": {}, - "outputs": [], - "source": [ - "data = build_data(inputdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17", - "metadata": {}, - "outputs": [], - "source": [ - "data.nbytes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18", - "metadata": {}, - "outputs": [], - "source": [ - "jax.debug.visualize_array_sharding(data)\n" - ] - }, { "cell_type": "markdown", - "id": "19", + "id": "13", "metadata": {}, "source": [ "try the sharding now with pipeline functions. since the pipeline functions use other data, I don´t use them directly, but build simplified versions here that only include stars. this involves the build up of the pipeline from the ground up in such a way that the data is sharded once and then we don´t have to touch it again" @@ -532,7 +469,7 @@ }, { "cell_type": "markdown", - "id": "20", + "id": "14", "metadata": {}, "source": [ "TODO: make sure the functions have the correct static argnums such that we don´t have to worry about the tracing shit" @@ -541,30 +478,45 @@ { "cell_type": "code", "execution_count": null, - "id": "21", + "id": "15", "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", + "\n", "from functools import partial\n", - "from pipe import Pipe" + "from pipe import Pipe\n", + "from rubix.galaxy.alignment import moment_of_inertia_tensor, rotation_matrix_from_inertia_tensor, apply_init_rotation, apply_rotation\n", + "from rubix.core.telescope import get_spatial_bin_edges\n", + "from rubix.telescope.utils import mask_particles_outside_aperture\n", + "from rubix.core.pipeline import RubixPipeline \n", + "from rubix.core.data import RubixData\n", + "from rubix.core.telescope import get_telescope\n", + "from jax import random as jrandom\n", + "from rubix.core.ssp import get_ssp, get_lookup_interpolation\n", + "from rubix.telescope.psf.kernels import gaussian_kernel_2d\n", + "from jax.scipy.signal import convolve2d\n", + "from rubix.telescope.lsf.lsf import _get_kernel\n", + "from jax.scipy.signal import convolve\n", + "from rubix import config as rubix_config" ] }, { "cell_type": "markdown", - "id": "22", + "id": "16", "metadata": {}, "source": [ - "galaxy rotation" + "## galaxy rotation" ] }, { "cell_type": "code", "execution_count": null, - "id": "23", + "id": "17", "metadata": {}, "outputs": [], "source": [ - "from rubix.galaxy.alignment import moment_of_inertia_tensor, rotation_matrix_from_inertia_tensor, apply_init_rotation, apply_rotation\n", + "# NBVAL_SKIP\n", "\n", "def rotate_galaxy_impl(data: jnp.array, alpha, beta, gamma)->jnp.array: \n", "\n", @@ -572,48 +524,29 @@ " R = rotation_matrix_from_inertia_tensor(I)\n", " data = data.at[coords_idx()].set(apply_rotation(apply_init_rotation(coords(data), R), alpha, beta, gamma))\n", " data = data.at[velocity_idx()].set(apply_rotation(apply_init_rotation(velocity(data), R), alpha, beta, gamma))\n", - " return data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24", - "metadata": {}, - "outputs": [], - "source": [ - "r = rotate_galaxy_impl(data, 0.1, 0.2, 0.3)\n", - "type(r), r.shape, r.dtype, r.nbytes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25", - "metadata": {}, - "outputs": [], - "source": [ + " return data\n", + "\n", + "# TODO: generalize, get these numbers from the config\n", "rotate_galaxy = partial(rotate_galaxy_impl, alpha=90.0, beta=0.0, gamma=0.0)" ] }, { "cell_type": "markdown", - "id": "26", + "id": "18", "metadata": {}, "source": [ - "filter particles" + "## filter particles" ] }, { "cell_type": "code", "execution_count": null, - "id": "27", + "id": "19", "metadata": {}, "outputs": [], "source": [ - "from rubix.core.telescope import get_spatial_bin_edges\n", - "from rubix.telescope.utils import mask_particles_outside_aperture\n", "\n", + "# NBVAL_SKIP\n", "\n", "def filter_particles_impl(data: jnp.ndarray, spatial_bin_edges) -> jnp.ndarray:\n", " mask = mask_particles_outside_aperture(\n", @@ -632,264 +565,512 @@ "filter_particles = partial(filter_particles_impl, spatial_bin_edges=get_spatial_bin_edges(config))" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "28", - "metadata": {}, - "outputs": [], - "source": [ - "get_spatial_bin_edges(config).shape " - ] - }, { "cell_type": "markdown", - "id": "29", + "id": "20", "metadata": {}, "source": [ - "try it out" + "## spaxel assignment" ] }, { "cell_type": "code", "execution_count": null, - "id": "30", + "id": "21", "metadata": {}, "outputs": [], "source": [ - "data = filter_particles(data)" + "# NBVAL_SKIP\n", + "\n", + "def spaxel_assignment_square_impl(data: jnp.ndarray, spatial_bin_edges)-> jnp.ndarray:\n", + " # Calculate assignment of of x and y coordinates to bins separately\n", + " x_indices = (\n", + " jnp.digitize(data[coords_idx()][:, 0], spatial_bin_edges) - 1\n", + " ) # -1 to start indexing at 0\n", + " y_indices = jnp.digitize(data[coords_idx()][:, 1], spatial_bin_edges) - 1\n", + "\n", + " number_of_bins = len(spatial_bin_edges) - 1\n", + "\n", + " # Clip the indices to the valid range\n", + " x_indices = jnp.clip(x_indices, 0, number_of_bins - 1)\n", + " y_indices = jnp.clip(y_indices, 0, number_of_bins - 1)\n", + "\n", + " # Flatten the 2D indices to 1D indices\n", + " pixel_positions = x_indices + (number_of_bins * y_indices)\n", + " return data.at[pixel_assignment_idx()].set(jnp.round(pixel_positions))\n", + "\n", + "\n", + "spaxel_assignment = partial(spaxel_assignment_square_impl, spatial_bin_edges=get_spatial_bin_edges(config))\n" ] }, { "cell_type": "markdown", - "id": "31", - "metadata": {}, - "source": [ - "try out simple pipeline " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32", + "id": "22", "metadata": {}, - "outputs": [], "source": [ - "data = inputdata | Pipe(build_data) | Pipe(rotate_galaxy) | Pipe(filter_particles)" + "## Calculate spectra" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "33", + "cell_type": "markdown", + "id": "23", "metadata": {}, - "outputs": [], "source": [ - "data.shape, data.nbytes / 1024**2, data.dtype" + "calculate spectra now. since this is so big, it would perpaps make sense to have a separate path for this thing instead of having to save this and drag it around all the time. " ] }, { "cell_type": "code", "execution_count": null, - "id": "34", + "id": "24", "metadata": {}, "outputs": [], "source": [ - "jax.debug.visualize_array_sharding(data)" + "# NBVAL_SKIP\n", + "\n", + "# this needs to be optimized, it uses far too much memory\n", + "def calculate_spectra_impl(data: jnp.ndarray, lookup_interpolation) -> jnp.ndarray: \n", + " print(\"Calculating spectra\")\n", + " print(\"Data shape:\", data.shape)\n", + " print(\"lookup type: \", type(lookup_interpolation))\n", + " print(\"lookup shape: \", lookup_interpolation.shape)\n", + " # this thing is gigantic and probably cannot be stored in memory for serious data\n", + " return data.at[spectra_index()].set(lookup_interpolation(\n", + " data[metallicity_idx()],\n", + " data[age_idx()],\n", + " ))\n", + "# this creates a file access that should not be on the hot path. \n", + "lookup_interpolation = get_lookup_interpolation(config)\n", + "calculate_spectra = partial(calculate_spectra_impl, lookup_interpolation=lookup_interpolation)" ] }, { "cell_type": "markdown", - "id": "35", - "metadata": {}, - "source": [ - "try to compile it and run it then,then check sharding" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36", + "id": "25", "metadata": {}, - "outputs": [], "source": [ - "from rubix.core.pipeline import RubixPipeline \n", - "from rubix.core.data import RubixData" + "## scale spectrum by mass" ] }, { "cell_type": "code", "execution_count": null, - "id": "37", + "id": "26", "metadata": {}, "outputs": [], "source": [ - "@jax.jit \n", - "def pipeline(data: jnp.array) -> jnp.ndarray:\n", - " data = rotate_galaxy(data)\n", - " data = filter_particles(data)\n", - " return data" + "# NBVAL_SKIP\n", + "\n", + "def scale_spectrum_by_mass(data: jnp.ndarray) -> jnp.ndarray:\n", + "\n", + " return data.at[spectra_index()].set(\n", + " data[spectra_index()] * data[mass_idx()][:, jnp.newaxis]\n", + " )" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "38", + "cell_type": "markdown", + "id": "27", "metadata": {}, - "outputs": [], "source": [ - "data = build_data(inputdata)\n", - "data = pipeline(data)" + "## doppler shift" ] }, { "cell_type": "code", "execution_count": null, - "id": "39", + "id": "28", "metadata": {}, "outputs": [], "source": [ - "data.shape, data.nbytes / 1024**2, data.dtype" + "# NBVAL_SKIP\n", + "\n", + "# get all the needed crap... \n", + "velocity_direction = rubix_config[\"ifu\"][\"doppler\"][\"velocity_direction\"]\n", + "directions = {\"x\": 0, \"y\": 1, \"z\": 2}" ] }, { "cell_type": "code", "execution_count": null, - "id": "40", + "id": "29", "metadata": {}, "outputs": [], "source": [ - "jax.debug.visualize_array_sharding(data)" + "# NBVAL_SKIP\n", + "# TODO: this needs to be fused with the resampling step such that the giant temporary array is not created\n", + "def apply_doppler_impl(data: jnp.ndarray, wavelength, c, direction) -> jnp.ndarray:\n", + "\n", + " # 3 is the index of the first velocity component\n", + " d = jnp.exp(data[:, 3 + direction]/ c) # 3 is offset of the velocity component\n", + "\n", + " return jax.vmap(lambda d: wavelength * d)(d)\n", + "\n", + "ssp = get_ssp(config)\n", + "ssp_wave= ssp.wavelength\n", + "direction = directions[velocity_direction]\n", + "cosmological_doppler_shift = (1 + config[\"galaxy\"][\"dist_z\"]) * ssp.wavelength\n", + "\n", + "apply_doppler = partial(apply_doppler_impl, wavelength=ssp_wave, c=3e8, direction=direction)" ] }, { "cell_type": "markdown", - "id": "41", + "id": "30", "metadata": {}, "source": [ - "spaxel assignment" + "## resampling" ] }, { "cell_type": "code", "execution_count": null, - "id": "42", + "id": "31", "metadata": {}, "outputs": [], "source": [ - "def spaxel_assignment_square_impl(data: jnp.ndarray, spatial_bin_edges)-> jnp.ndarray:\n", - " # Calculate assignment of of x and y coordinates to bins separately\n", - " x_indices = (\n", - " jnp.digitize(data[coords_idx()][:, 0], spatial_bin_edges) - 1\n", - " ) # -1 to start indexing at 0\n", - " y_indices = jnp.digitize(data[coords_idx()][:, 1], spatial_bin_edges) - 1\n", + "# NBVAL_SKIP\n", + "def calculate_diff(\n", + " vec, pad_with_zero: bool = True\n", + "):\n", + " \"\"\"\n", + " Calculate the difference between each element in a vector.\n", "\n", - " number_of_bins = len(spatial_bin_edges) - 1\n", + " Args:\n", + " vec (array-like): The input vector.\n", + " pad_with_zero (bool, optional): Whether to prepend the first element of the vector to the differences. Default is True.\n", "\n", - " # Clip the indices to the valid range\n", - " x_indices = jnp.clip(x_indices, 0, number_of_bins - 1)\n", - " y_indices = jnp.clip(y_indices, 0, number_of_bins - 1)\n", + " Returns:\n", + " The differences between each element in the vector (array-like).\n", + " \"\"\"\n", "\n", - " # Flatten the 2D indices to 1D indices\n", - " pixel_positions = x_indices + (number_of_bins * y_indices)\n", - " return data.at[pixel_assignment_idx()].set(jnp.round(pixel_positions))\n", + " if pad_with_zero:\n", + " differences = jnp.diff(vec, prepend=vec[0])\n", + " else:\n", + " differences = jnp.diff(vec)\n", + " return differences\n", "\n", "\n", - "spaxel_assignment = partial(spaxel_assignment_square_impl, spatial_bin_edges=get_spatial_bin_edges(config))\n" + "def resample_spectrum_impl(init_spectrum: jnp.ndarray, initial_wavelength, target_wavelength) -> jnp.ndarray:\n", + " in_range_mask = (initial_wavelength >= jnp.min(target_wavelength)) & (\n", + " initial_wavelength <= jnp.max(target_wavelength)\n", + " )\n", + "\n", + " intrinsic_wave_diff = calculate_diff(initial_wavelength) * in_range_mask\n", + "\n", + " # Get total luminsoity within the wavelength range\n", + " total_lum = jnp.sum(init_spectrum * intrinsic_wave_diff)\n", + "\n", + " # Interpolate the wavelegnth to the telescope grid\n", + " particle_lum = jnp.interp(target_wavelength, initial_wavelength, init_spectrum)\n", + "\n", + " # New total luminosity\n", + " new_total_lum = jnp.sum(particle_lum * calculate_diff(target_wavelength))\n", + "\n", + " # Factor to conserve flux in the new spectrum\n", + " scale_factor = total_lum / new_total_lum\n", + " scale_factor = jnp.nan_to_num(\n", + " scale_factor, nan=0.0\n", + " ) # Otherwise we get NaNs if new_total_lum is zero\n", + " lum = particle_lum * scale_factor\n", + "\n", + " return lum\n", + "\n", + "# indexing stuff for spectra\n", + "def rs_spectra_index(out_size: int): \n", + " return jnp.s_[:, 16:(16 + out_size)]\n", + "\n", + "def diff_spectra_index(in_size: int, out_size: int): \n", + " return jnp.s_[:, 16:(16 + (in_size - out_size))]\n", + "\n", + "def rs_spectra(data: jnp.ndarray, out_size: int) -> jnp.ndarray:\n", + " \"\"\"\n", + " Spectra function to be used in the pipeline.\n", + " \"\"\"\n", + " return data[rs_spectra_index(out_size)]\n", + "\n", + "def doppler_and_resample(data: jnp.array, target_wavelength: jnp.array, out_size: int) -> jnp.ndarray:\n", + " \"\"\"\n", + " Doppler shift and resample the spectrum.\n", + " \"\"\"\n", + " # Apply the doppler shift\n", + " v = apply_doppler(data)\n", + "\n", + " # Resample the spectrum\n", + " data = data.at[rs_spectra_index(out_size)].set(\n", + " jax.vmap(resample_spectrum_impl, in_axes=(0,0, None))(\n", + " data[spectra_index()], v, target_wavelength\n", + " )\n", + " )\n", + " data = data.at[diff_spectra_index(ssp_wave.shape[0], out_size)].set(0.0)\n", + "\n", + " return data\n", + "\n", + "telescope = get_telescope(config)\n", + "telescope_wavelength = telescope.wave_seq\n", + "num_spaxels = int(telescope.sbin)\n", + "out_size = int(telescope_wavelength.shape[0])\n", + "\n", + "resample = partial(doppler_and_resample,target_wavelength=telescope_wavelength, out_size = telescope_wavelength.shape[0])" ] }, { "cell_type": "markdown", - "id": "43", + "id": "32", "metadata": {}, "source": [ - "try it out again" + "get all the telescope data stuff and make a partial" ] }, { "cell_type": "code", "execution_count": null, - "id": "44", + "id": "33", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "telescope = get_telescope(config)\n", + "telescope_wavelength = telescope.wave_seq\n", + "num_spaxels = int(telescope.sbin)\n", + "out_size = int(telescope_wavelength.shape[0])\n", + "\n", + "resample = partial(doppler_and_resample,target_wavelength=telescope_wavelength, out_size = telescope_wavelength.shape[0])" + ] + }, + { + "cell_type": "markdown", + "id": "34", + "metadata": {}, + "source": [ + "## apply extinction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35", "metadata": {}, "outputs": [], "source": [ - "data = inputdata | Pipe(build_data) | Pipe(rotate_galaxy) | Pipe(filter_particles) | Pipe(spaxel_assignment)" + "# NBVAL_SKIP\n", + "from rubix.telescope.utils import calculate_spatial_bin_edges\n", + "from rubix.core.cosmology import get_cosmology\n", + "from rubix.spectra.dust.extinction_models import Rv_model_dict, Cardelli89, Gordon23\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "45", + "id": "36", "metadata": {}, "outputs": [], "source": [ - "jax.debug.visualize_array_sharding(data)" + "# NBVAL_SKIP\n", + "galaxy_dist_z = config[\"galaxy\"][\"dist_z\"]\n", + "telescope = get_telescope(config)\n", + "telescope_wavelength = telescope.wave_seq\n", + "num_spaxels = int(telescope.sbin)\n", + "cosmology = get_cosmology(config)\n", + "ext_model = config[\"ssp\"][\"dust\"][\"extinction_model\"]\n", + "Rv = config[\"ssp\"][\"dust\"][\"Rv\"]\n", + "ext_model_class = Rv_model_dict[ext_model]\n", + "ext = ext_model_class(Rv=Rv)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "_, spatial_bin_size = calculate_spatial_bin_edges(fov =telescope.fov, spatial_bins = telescope.sbin, dist_z = galaxy_dist_z, cosmology = cosmology)\n", + "spaxel_area = spatial_bin_size**2\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "\n", + "def apply_extinction(data: jnp.ndarray, wavelength, spaxel_area, n_spaxels, ext) -> jnp.ndarray:\n", + " # I don´t have gas in the data currently, so I skip this for now. \n", + " # The way it is done in the dust_extinction module has config lookups within the function, and the sorting should be avoided when possible! It's not clear why this is needed? \n", + " pass\n", + " " ] }, { "cell_type": "markdown", - "id": "46", + "id": "39", "metadata": {}, "source": [ - "calculate spectra now. since this is so big, it would perpaps make sense to have a separate path for this thing instead of having to save this and drag it around all the time. " + "## calculate datacube" ] }, { "cell_type": "code", "execution_count": null, - "id": "47", + "id": "40", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "def calculate_datacube_impl(data: jnp.ndarray, num_spaxels: int, out_size: int) -> jnp.ndarray:\n", + " return jax.ops.segment_sum(\n", + " data[rs_spectra_index(out_size)], # spectra\n", + " data[pixel_assignment_idx()].astype('int32'), # pixel assignment\n", + " num_segments=num_spaxels**2,\n", + " ).reshape(\n", + " (num_spaxels, num_spaxels, telescope_wavelength.shape[0])\n", + " )\n", + "\n", + "calculate_datacube = partial(calculate_datacube_impl, num_spaxels= int(telescope.sbin), out_size=out_size)" + ] + }, + { + "cell_type": "markdown", + "id": "41", + "metadata": {}, + "source": [ + "## convolve psf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42", "metadata": {}, "outputs": [], "source": [ - "from rubix.core.ssp import get_ssp, get_lookup_interpolation" + "# NBVAL_SKIP\n", + "m, n = config[\"telescope\"][\"psf\"][\"size\"], config[\"telescope\"][\"psf\"][\"size\"]\n", + "sigma = config[\"telescope\"][\"psf\"][\"sigma\"]\n", + "kernel = gaussian_kernel_2d(m, n, sigma)" ] }, { "cell_type": "code", "execution_count": null, - "id": "48", + "id": "43", "metadata": {}, "outputs": [], "source": [ - "def calculate_spectra_impl(data: jnp.ndarray, lookup_interpolation) -> jnp.ndarray: \n", + "# NBVAL_SKIP\n", + "def apply_psf_impl(cube: jnp.ndarray, kernel) -> jnp.ndarray:\n", "\n", - " # this thing is gigantic and probably cannot be stored in memory for serious data\n", - " return data.at[spectra_index()].set(lookup_interpolation(\n", - " data[metallicity_idx()],\n", - " data[age_idx()],\n", - " ))\n" + " return jnp.transpose(jax.vmap(partial(convolve2d, mode = \"same\"), in_axes = (2, None))(\n", + " cube, \n", + " kernel,\n", + " ), (1, 2, 0))\n", + "apply_psf = partial(apply_psf_impl, kernel=kernel)" + ] + }, + { + "cell_type": "markdown", + "id": "44", + "metadata": {}, + "source": [ + "## convolve lsf" ] }, { "cell_type": "code", "execution_count": null, - "id": "49", + "id": "45", "metadata": {}, "outputs": [], "source": [ - "lookup_interpolation = get_lookup_interpolation(config)\n", + "# NBVAL_SKIP\n", + "sigma = config[\"telescope\"][\"lsf\"][\"sigma\"]\n", + "telescope = get_telescope(config)\n", + "wave_resolution = telescope.wave_res\n", + "extend_factor = 12\n", "\n", - "calculate_spectra = partial(calculate_spectra_impl, lookup_interpolation=lookup_interpolation)" + "kernel = _get_kernel(sigma, wave_resolution, factor=extend_factor)" ] }, { "cell_type": "code", "execution_count": null, - "id": "50", + "id": "46", "metadata": {}, "outputs": [], "source": [ - "data = inputdata | Pipe(build_data) | Pipe(rotate_galaxy) | Pipe(filter_particles) | Pipe(spaxel_assignment) | Pipe(calculate_spectra)" + "# NBVAL_SKIP\n", + "def apply_lsf_impl(cube: jnp.ndarray, kernel: jnp.array, extend_factor: int) -> jnp.ndarray:\n", + " reshaped_cube = cube.reshape(-1, cube.shape[-1])\n", + " convolved = jax.vmap(partial(convolve, mode=\"full\"), in_axes=(0, None))(reshaped_cube, kernel)\n", + " end = reshaped_cube.shape[1] + kernel.shape[0] - 1 - extend_factor\n", + " convolved= convolved[:, extend_factor:end]\n", + " return convolved.reshape(cube.shape)\n", + "\n", + "apply_lsf = partial(apply_lsf_impl, kernel=kernel, extend_factor=extend_factor)" + ] + }, + { + "cell_type": "markdown", + "id": "47", + "metadata": {}, + "source": [ + "## apply noise" ] }, { "cell_type": "code", "execution_count": null, - "id": "51", + "id": "48", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "signal_to_noise = config[\"telescope\"][\"noise\"][\"signal_to_noise\"]\n", + "\n", + "# Get the noise distribution\n", + "noise_distribution = config[\"telescope\"][\"noise\"][\"noise_distribution\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49", "metadata": {}, "outputs": [], "source": [ - "type(data), data.shape, data.dtype, data.nbytes / 1024**2" + "# NBVAL_SKIP\n", + "def calculate_S2N(cube: jnp.ndarray, observation_s2n: float)->jnp.ndarray: \n", + " flux_image = jnp.sum(cube, axis=2)\n", + " return jnp.where(flux_image > 0 , (jnp.sqrt(jnp.median(jnp.where(flux_image > 0 , flux_image, 0.)))/observation_s2n)/jnp.sqrt(flux_image), 0)\n", + "\n", + "def apply_noise_impl(cube: jnp.array, signal_to_noise: float) -> jnp.ndarray:\n", + " # TODO: this can probably be vmapped for better performance\n", + " key = jrandom.PRNGKey(0)\n", + " s2n = calculate_S2N(cube, signal_to_noise)\n", + " return cube + cube*jrandom.normal(key, cube.shape) * s2n[:, :, None] \n", + "\n", + "apply_noise = partial(apply_noise_impl, signal_to_noise=signal_to_noise)\n" + ] + }, + { + "cell_type": "markdown", + "id": "50", + "metadata": {}, + "source": [ + "## build pipelines" + ] + }, + { + "cell_type": "markdown", + "id": "51", + "metadata": {}, + "source": [ + "looks like everything is in place now, so we can build pipelines for the data transformations and the cube transformations. This is only done for sake of debugging, in production the separation is not needed" ] }, { @@ -899,7 +1080,16 @@ "metadata": {}, "outputs": [], "source": [ - "jax.debug.visualize_array_sharding(data)" + "# NBVAL_SKIP\n", + "@jax.jit\n", + "def transform_data(inputdata: jnp.ndarray) -> jnp.ndarray:\n", + "\n", + " data = rotate_galaxy(inputdata)\n", + " data = filter_particles(data)\n", + " data = spaxel_assignment(data)\n", + " data = calculate_spectra(data)\n", + " data = scale_spectrum_by_mass(data)\n", + " return data" ] }, { @@ -907,7 +1097,7 @@ "id": "53", "metadata": {}, "source": [ - "scale spectrum by mass" + "this pipeline building and data prepare needs to go eventually" ] }, { @@ -917,11 +1107,9 @@ "metadata": {}, "outputs": [], "source": [ - "def scale_spectrum_by_mass(data: jnp.ndarray) -> jnp.ndarray:\n", - "\n", - " return data.at[spectra_index()].set(\n", - " data[spectra_index()] * data[mass_idx()][:, jnp.newaxis]\n", - " )" + "# NBVAL_SKIP\n", + "pipe = RubixPipeline(config)\n", + "inputdata = pipe.prepare_data()" ] }, { @@ -931,7 +1119,8 @@ "metadata": {}, "outputs": [], "source": [ - "data = scale_spectrum_by_mass(data)" + "# NBVAL_SKIP\n", + "data = inputdata | Pipe(build_data)" ] }, { @@ -941,15 +1130,19 @@ "metadata": {}, "outputs": [], "source": [ - "type(data), data.shape, data.dtype, data.nbytes / 1024**2" + "# NBVAL_SKIP\n", + "jax.debug.visualize_array_sharding(data)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "id": "57", "metadata": {}, + "outputs": [], "source": [ - "So far, we barely need 710 MB for everything we do, and we are not efficient at all wrt memory. On multiple GPUs with overall 100GB, we should easily be able to process the required data sizes? " + "# NBVAL_SKIP\n", + "data = transform_data(data)" ] }, { @@ -959,15 +1152,20 @@ "metadata": {}, "outputs": [], "source": [ - "jax.debug.visualize_array_sharding(data)" + "# NBVAL_SKIP\n", + "data.block_until_ready();" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "id": "59", "metadata": {}, + "outputs": [], "source": [ - "doppler shift" + "# NBVAL_SKIP\n", + "\n", + "data.shape, data.nbytes// 1024**2, data.nbytes/1024**3" ] }, { @@ -977,68 +1175,310 @@ "metadata": {}, "outputs": [], "source": [ - "# get all the needed crap... \n", - "from rubix import config as rubix_config\n", - "velocity_direction = rubix_config[\"ifu\"][\"doppler\"][\"velocity_direction\"]\n", - "directions = {\"x\": 0, \"y\": 1, \"z\": 2}" + "# NBVAL_SKIP\n", + "\n", + "jax.debug.visualize_array_sharding(data)" + ] + }, + { + "cell_type": "markdown", + "id": "61", + "metadata": {}, + "source": [ + "The data array is still correctly sharded. yay!" + ] + }, + { + "cell_type": "markdown", + "id": "62", + "metadata": {}, + "source": [ + "when working with the cube pipeline now, we have to reshard it first and index into the padded cube or pad all the other data too. This is done in the `compute_cube` function using the first method" ] }, { "cell_type": "code", "execution_count": null, - "id": "61", + "id": "63", "metadata": {}, "outputs": [], "source": [ - "velocity_direction" + "# NBVAL_SKIP\n", + "def reshard_cube(cube: jnp.ndarray,) -> jnp.ndarray:\n", + " d = cube.shape[2]\n", + "\n", + " # we can only go upwards to not loose\n", + " while d % jax.device_count() != 0:\n", + " d += 1\n", + " d\n", + " padding = d - cube.shape[2]\n", + " mesh = jax.make_mesh((jax.device_count(), ), ('devices',))\n", + " shard = NamedSharding(mesh, P(None, None, 'devices'))\n", + "\n", + " cube = jax.device_put(jnp.pad(cube, ((0, 0), (0, 0), (0, padding))), shard)\n", + " return cube\n", + "\n", + "def compute_cube(inputdata: jnp.ndarray) -> jnp.ndarray:\n", + " cube = calculate_datacube(inputdata)\n", + " \n", + " # not sure if this counteracts the sharding\n", + " cube = apply_psf(cube)\n", + " cube = apply_lsf(cube)\n", + " cube = apply_noise(cube)\n", + " return cube\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "64", + "metadata": {}, + "source": [ + "simple cube is not sharded" ] }, { "cell_type": "code", "execution_count": null, - "id": "62", + "id": "65", "metadata": {}, "outputs": [], "source": [ - "def apply_doppler_impl(data: jnp.ndarray, wavelength, c, direction) -> jnp.ndarray:\n", - " print(\"shapes: \", data[velocity_idx()].shape, wavelength.shape)\n", + "# NBVAL_SKIP\n", + "cube = calculate_datacube(data)\n", + "jax.debug.visualize_array_sharding(cube.reshape(cube.shape[0]* cube.shape[1], cube.shape[2]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "cube = reshard_cube(cube)\n", + "jax.debug.visualize_array_sharding(cube.reshape(cube.shape[0]* cube.shape[1], cube.shape[2]))" + ] + }, + { + "cell_type": "markdown", + "id": "67", + "metadata": {}, + "source": [ + "I have not applied this to the computation now because it is messy to do and it's not the main objective. this data cube is tiny by comparison. What one has to do is pad the data that takes part in the computations in the cube pipeline to the size of the cube. then the sharding should be fine. indexing into the cube will destroy the sharding again apparently, distributing it over all devices in the case of this tiny one. not good... " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "final_cube = compute_cube(data)\n", + "final_cube.block_until_ready()\n", + "jax.debug.visualize_array_sharding(final_cube.reshape(final_cube.shape[0]* final_cube.shape[1], final_cube.shape[2]))" + ] + }, + { + "cell_type": "markdown", + "id": "69", + "metadata": {}, + "source": [ + "not sharded correctly... :/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", "\n", - " # FIXME: this needs to be vmapped or broadcasted in such a way that every velocity component is doppler shifted for each wavelength. \n", - " # calculate classic doppler shift \n", - " v = data[velocity_idx()][:, direction]\n", - " return data.at[velocity_idx()][:, direction].set(\n", - " wavelength * jnp.exp(v/c)\n", - " )\n", + "final_cube.shape, final_cube.nbytes / 1024**2, final_cube.dtype" + ] + }, + { + "cell_type": "markdown", + "id": "71", + "metadata": {}, + "source": [ + "... but it's also really small, so might be that? " + ] + }, + { + "cell_type": "markdown", + "id": "72", + "metadata": {}, + "source": [ + "## memory usage " + ] + }, + { + "cell_type": "markdown", + "id": "73", + "metadata": {}, + "source": [ + "The main point: which function causes memory explosion and why? " + ] + }, + { + "cell_type": "markdown", + "id": "74", + "metadata": {}, + "source": [ + "So far, we barely need 710 MB for the data cube, and we are not efficiently using memory at all. On multiple GPUs with overall O(100)GB, we should easily be able to process the required data sizes.\n", "\n", - "ssp = get_ssp(config)\n", - "ssp_wave= ssp.wavelength\n", - "direction = directions[velocity_direction]\n", - "cosmological_doppler_shift = (1 + config[\"galaxy\"][\"dist_z\"]) * ssp.wavelength\n", + "**Expectation:**\n", + "For the 500k particles, this would amount to roughly (500/30)*710 = 11833, so 12 GB. Even with with double the number of spectral lines we should easily be able to run this on a 4090. up to ~800k particles on a single GPU with the current spectral line number should also be doable, and we do not talk about sharding here at all. \n", "\n", - "apply_doppler = partial(apply_doppler_impl, wavelength=ssp_wave, c=3e8, direction=direction)" + "When we have gas, this goes down by half. At any rate, how can this computation cause memory issues on this gpu?\n", + "\n", + "**Observation**\n", + "However, something temporarily causes a gigantic number of allocations in temporary arrays that lets memory usage go up to 40G or more. this is the killer element, I don't think that the sharding as such is a problem. \n", + "\n", + "Experiments above show that it's happening when processing the data itself, the cube computations are harmless." + ] + }, + { + "cell_type": "markdown", + "id": "75", + "metadata": {}, + "source": [ + "check each function of the pipeline with htop/nvtop or similar tools: htop -d 3 --> update ever 0.3 seconds" ] }, { "cell_type": "code", "execution_count": null, - "id": "63", + "id": "76", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "data = build_data(inputdata)\n", + "data.block_until_ready(); # not the culprit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "data = rotate_galaxy(data)\n", + "data.block_until_ready(); #not the culprit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "data = filter_particles(data)\n", + "data.block_until_ready(); #not the culprit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "data = spaxel_assignment(data)\n", + "data.block_until_ready(); #not the culprit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "data = calculate_spectra(data)\n", + "data.block_until_ready(); # very much the culprit! increases memory size to > 40 GB even though the input is only ~0.7 - 0.8 GB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "data = scale_spectrum_by_mass(data)\n", + "data.block_until_ready(); #not the culprit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "data = resample(data)\n", + "data.block_until_ready(); # moderate increase, not beyond a manageable size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83", "metadata": {}, "outputs": [], "source": [ - "apply_doppler(data)" + "# NBVAL_SKIP\n", + "cube = calculate_datacube(data)\n", + "cube.block_until_ready(); #not the culprit" ] }, { "cell_type": "markdown", - "id": "64", + "id": "84", "metadata": {}, "source": [ - "resampling" + "just to be sure: check cube computation agani" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "final_cube = compute_cube(data)\n", + "final_cube.block_until_ready(); #not the culprit at all" ] }, { "cell_type": "markdown", - "id": "65", + "id": "86", + "metadata": {}, + "source": [ + "There is a big problem in the spectra calculation that causes an enormous temporary memory issue. " + ] + }, + { + "cell_type": "markdown", + "id": "87", "metadata": {}, "source": [] } From dceb56057363669b46f69dbe36a3ae9572cf831e Mon Sep 17 00:00:00 2001 From: anschaible Date: Mon, 12 May 2025 16:22:14 +0200 Subject: [PATCH 19/76] pipeline execution .py file --- notebooks/debug_spectra_lookup.ipynb | 445 ++++++++++++++++++ notebooks/rubix_pipeline_sharding.py | 123 +++++ ...x_pipeline_single_function_shard_map.ipynb | 37 +- rubix/core/ifu.py | 13 +- rubix/core/ssp.py | 1 + 5 files changed, 607 insertions(+), 12 deletions(-) create mode 100644 notebooks/debug_spectra_lookup.ipynb diff --git a/notebooks/debug_spectra_lookup.ipynb b/notebooks/debug_spectra_lookup.ipynb new file mode 100644 index 00000000..7bf87a42 --- /dev/null +++ b/notebooks/debug_spectra_lookup.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "edac2421", + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1, 2, 3, 4 '\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c5ba1ca6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-08 23:07:50,850 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-05-08 23:07:50,853 - rubix - INFO - Rubix version: 0.0.post417+g76e9abf.d20250424\n", + "2025-05-08 23:07:50,853 - rubix - INFO - JAX version: 0.6.0\n", + "2025-05-08 23:07:50,854 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "config = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 14,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 400000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-14.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"FSPS\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5a85bfe9", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "\n", + "n_particles = 400_000\n", + "\n", + "age = jnp.linspace(0, 20, n_particles, )\n", + "metallicity = jnp.linspace(0., 0.05, n_particles, )\n", + "\n", + "from jax.sharding import Mesh, PartitionSpec as P\n", + "from jax.experimental import shard_map\n", + "from jax.sharding import NamedSharding\n", + "\n", + "\n", + "\n", + "devices = jax.devices()\n", + "mesh = Mesh(devices, axis_names=('N_particles',))\n", + "sharding = NamedSharding(mesh, P('N_particles')) \n", + "\n", + "age = jax.device_put(age, sharding)\n", + "metallicity = jax.device_put(metallicity, sharding)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e857c3de", + "metadata": {}, + "outputs": [], + "source": [ + "age = jnp.atleast_1d(age)\n", + "metallicity = jnp.atleast_1d(metallicity)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3f181ff3", + "metadata": {}, + "outputs": [], + "source": [ + "from rubix.core.ssp import get_lookup_interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8016babb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-08 23:08:16,661 - rubix - DEBUG - Method not defined, using default method: cubic\n" + ] + } + ], + "source": [ + "lookup_interpolation = get_lookup_interpolation(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0622cce9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lookup_interpolation Partial(>, method='cubic', x=Array([4.4904351e-05, 1.4200003e-04, 2.5251572e-04, 4.4904352e-04,\n", + " 7.9852482e-04, 1.4200003e-03, 2.5251573e-03, 4.4904351e-03,\n", + " 7.9852482e-03, 1.4199999e-02, 2.5251566e-02, 4.4904340e-02], dtype=float32), y=Array([1.00000005e-04, 1.12201837e-04, 1.25892519e-04, 1.41253782e-04,\n", + " 1.58489333e-04, 1.77827940e-04, 1.99526214e-04, 2.23872063e-04,\n", + " 2.51188700e-04, 2.81838322e-04, 3.16227757e-04, 3.54813354e-04,\n", + " 3.98107077e-04, 4.46683698e-04, 5.01187285e-04, 5.62341302e-04,\n", + " 6.30957307e-04, 7.07945612e-04, 7.94328400e-04, 8.91251024e-04,\n", + " 1.00000005e-03, 1.12201832e-03, 1.25892507e-03, 1.41253788e-03,\n", + " 1.58489333e-03, 1.77827943e-03, 1.99526199e-03, 2.23872066e-03,\n", + " 2.51188688e-03, 2.81838328e-03, 3.16227763e-03, 3.54813342e-03,\n", + " 3.98107106e-03, 4.46683681e-03, 5.01187285e-03, 5.62341325e-03,\n", + " 6.30957261e-03, 7.07945647e-03, 7.94328377e-03, 8.91251024e-03,\n", + " 9.99999978e-03, 1.12201832e-02, 1.25892544e-02, 1.41253741e-02,\n", + " 1.58489328e-02, 1.77827943e-02, 1.99526213e-02, 2.23872140e-02,\n", + " 2.51188632e-02, 2.81838328e-02, 3.16227749e-02, 3.54813337e-02,\n", + " 3.98107208e-02, 4.46683578e-02, 5.01187295e-02, 5.62341325e-02,\n", + " 6.30957261e-02, 7.07945824e-02, 7.94328228e-02, 8.91251042e-02,\n", + " 1.00000001e-01, 1.12201847e-01, 1.25892550e-01, 1.41253740e-01,\n", + " 1.58489317e-01, 1.77827939e-01, 1.99526235e-01, 2.23872125e-01,\n", + " 2.51188636e-01, 2.81838298e-01, 3.16227764e-01, 3.54813397e-01,\n", + " 3.98107171e-01, 4.46683586e-01, 5.01187205e-01, 5.62341332e-01,\n", + " 6.30957365e-01, 7.07945764e-01, 7.94328213e-01, 8.91250908e-01,\n", + " 1.00000000e+00, 1.12201846e+00, 1.25892544e+00, 1.41253757e+00,\n", + " 1.58489323e+00, 1.77827942e+00, 1.99526238e+00, 2.23872113e+00,\n", + " 2.51188660e+00, 2.81838274e+00, 3.16227770e+00, 3.54813409e+00,\n", + " 3.98107195e+00, 4.46683550e+00, 5.01187229e+00, 5.62341309e+00,\n", + " 6.30957365e+00, 7.07945824e+00, 7.94328213e+00, 8.91250896e+00,\n", + " 1.00000000e+01, 1.12201834e+01, 1.25892544e+01, 1.41253748e+01,\n", + " 1.58489332e+01, 1.77827950e+01, 1.99526215e+01], dtype=float32), f=Array([[[3.69801944e-25, 1.71711785e-25, 1.01008924e-25, ...,\n", + " 4.20808249e-11, 4.13591869e-11, 4.06485991e-11],\n", + " [2.95627621e-25, 1.37270093e-25, 8.07487082e-26, ...,\n", + " 3.36403162e-11, 3.30634235e-11, 3.24953640e-11],\n", + " [3.62052076e-25, 1.68113235e-25, 9.88920961e-26, ...,\n", + " 4.11989401e-11, 4.04924289e-11, 3.97967319e-11],\n", + " ...,\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.87085782e-21, 6.62186151e-21, 6.38153848e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.64786358e-21, 6.40694763e-21, 6.17442546e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.16116230e-21, 5.93788412e-21, 5.72238539e-21]],\n", + "\n", + " [[2.47674418e-25, 1.15003767e-25, 6.76506097e-26, ...,\n", + " 2.81835822e-11, 2.77002657e-11, 2.72243512e-11],\n", + " [2.70331983e-25, 1.25524459e-25, 7.38393714e-26, ...,\n", + " 3.07618514e-11, 3.02343220e-11, 2.97148695e-11],\n", + " [3.59155428e-25, 1.66768228e-25, 9.81008994e-26, ...,\n", + " 4.08693253e-11, 4.01684623e-11, 3.94783338e-11],\n", + " ...,\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 8.89014787e-21, 8.56769041e-21, 8.25660749e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 8.48373481e-21, 8.17602604e-21, 7.87917160e-21],\n", + " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 6.92724666e-21, 6.67610364e-21, 6.43376153e-21]],\n", + "\n", + " [[2.81423712e-25, 1.30674723e-25, 7.68690040e-26, ...,\n", + " 3.20240119e-11, 3.14748366e-11, 3.09340713e-11],\n", + " [2.62237481e-25, 1.21765895e-25, 7.16284100e-26, ...,\n", + " 2.98407549e-11, 2.93290184e-11, 2.88251211e-11],\n", + " [2.73612140e-25, 1.27047540e-25, 7.47353201e-26, ...,\n", + " 3.11351084e-11, 3.06011778e-11, 3.00754213e-11],\n", + " ...,\n", + " [1.32808084e-10, 1.46964954e-10, 1.78483950e-10, ...,\n", + " 8.73207478e-21, 8.41537447e-21, 8.10981686e-21],\n", + " [5.43437206e-09, 5.32875122e-09, 5.96376948e-09, ...,\n", + " 8.36363771e-21, 8.06031204e-21, 7.76765247e-21],\n", + " [9.67010383e-09, 9.52066159e-09, 1.06839435e-08, ...,\n", + " 7.63764698e-21, 7.36066264e-21, 7.09341060e-21]],\n", + "\n", + " ...,\n", + "\n", + " [[3.35264627e-25, 1.55674908e-25, 9.15752880e-26, ...,\n", + " 3.81507222e-11, 3.74964816e-11, 3.68522574e-11],\n", + " [3.28182234e-25, 1.52386307e-25, 8.96407792e-26, ...,\n", + " 3.73447974e-11, 3.67043756e-11, 3.60737620e-11],\n", + " [3.33899035e-25, 1.55040824e-25, 9.12022862e-26, ...,\n", + " 3.79953291e-11, 3.73437531e-11, 3.67021517e-11],\n", + " ...,\n", + " [8.59993882e-11, 1.15849191e-10, 1.53091637e-10, ...,\n", + " 1.75658879e-20, 1.69284419e-20, 1.63133959e-20],\n", + " [4.22778722e-11, 5.95679478e-11, 7.97761926e-11, ...,\n", + " 1.53844511e-20, 1.48262221e-20, 1.42875434e-20],\n", + " [2.23467148e-11, 3.41603967e-11, 4.68701189e-11, ...,\n", + " 1.51603821e-20, 1.46103038e-20, 1.40795349e-20]],\n", + "\n", + " [[3.28365965e-25, 1.52471627e-25, 8.96909643e-26, ...,\n", + " 3.73657043e-11, 3.67249252e-11, 3.60939577e-11],\n", + " [3.06845691e-25, 1.42479015e-25, 8.38128412e-26, ...,\n", + " 3.49168507e-11, 3.43180658e-11, 3.37284506e-11],\n", + " [3.15609887e-25, 1.46548539e-25, 8.62067320e-26, ...,\n", + " 3.59141536e-11, 3.52982678e-11, 3.46918119e-11],\n", + " ...,\n", + " [4.62700885e-11, 7.34858702e-11, 1.02663184e-10, ...,\n", + " 1.95786429e-20, 1.88677474e-20, 1.81815365e-20],\n", + " [5.22229378e-11, 7.36018815e-11, 9.85654544e-11, ...,\n", + " 1.83188566e-20, 1.76538179e-20, 1.70122553e-20],\n", + " [3.85684748e-11, 5.60884464e-11, 7.57659144e-11, ...,\n", + " 1.75572090e-20, 1.69198550e-20, 1.63050046e-20]],\n", + "\n", + " [[3.02441580e-25, 1.40434041e-25, 8.26098899e-26, ...,\n", + " 3.44156960e-11, 3.38255049e-11, 3.32443517e-11],\n", + " [3.15393394e-25, 1.46448021e-25, 8.61475921e-26, ...,\n", + " 3.58895171e-11, 3.52740545e-11, 3.46680150e-11],\n", + " [3.26780774e-25, 1.51735571e-25, 8.92579844e-26, ...,\n", + " 3.71853208e-11, 3.65476364e-11, 3.59197151e-11],\n", + " ...,\n", + " [3.19179711e-22, 1.78760657e-21, 5.73130869e-21, ...,\n", + " 1.93975841e-20, 1.86948069e-20, 1.80162264e-20],\n", + " [6.14501020e-11, 8.57527313e-11, 1.14455764e-10, ...,\n", + " 2.07055004e-20, 1.99535222e-20, 1.92279652e-20],\n", + " [1.24949378e-12, 2.30248633e-12, 3.44144765e-12, ...,\n", + " 2.00280460e-20, 1.93006602e-20, 1.85987619e-20]]], dtype=float32), extrap=0)\n" + ] + } + ], + "source": [ + "print(\"lookup_interpolation\", lookup_interpolation)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "fe481d17", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-08 23:08:20.694754: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:652] The byte size of input/output arguments (9621985660) exceeds the base limit (8654290944). This indicates an error in the calculation!\n", + "2025-05-08 23:08:20.703903: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3021] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 8.94GiB (9597600124 bytes), down from 8.94GiB (9597600124 bytes) originally\n", + "2025-05-08 23:08:31.169703: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_0_bfc) ran out of memory trying to allocate 8.93GiB (rounded to 9590400000)requested by op \n", + "2025-05-08 23:08:31.169909: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] **__________________________________________________________________________________________________\n", + "E0508 23:08:31.169937 2740412 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes. [tf-allocator-allocation-error='']\n", + "2025-05-08 23:08:31.170372: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_1_bfc) ran out of memory trying to allocate 8.93GiB (rounded to 9590400000)requested by op \n", + "2025-05-08 23:08:31.170537: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *___________________________________________________________________________________________________\n", + "E0508 23:08:31.170558 2740415 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes. [tf-allocator-allocation-error='']\n", + "2025-05-08 23:08:31.171601: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_2_bfc) ran out of memory trying to allocate 8.93GiB (rounded to 9590400000)requested by op \n", + "2025-05-08 23:08:31.171784: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *___________________________________________________________________________________________________\n", + "E0508 23:08:31.171805 2740418 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes. [tf-allocator-allocation-error='']\n", + "2025-05-08 23:08:31.173120: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_3_bfc) ran out of memory trying to allocate 8.93GiB (rounded to 9590400000)requested by op \n", + "2025-05-08 23:08:31.173458: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *___________________________________________________________________________________________________\n", + "E0508 23:08:31.173499 2740421 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes. [tf-allocator-allocation-error='']\n" + ] + }, + { + "ename": "XlaRuntimeError", + "evalue": "RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes.: while running replica 0 and partition 0 of a replicated computation (other replicas may have failed as well).", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mXlaRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m age, metallicity \u001b[38;5;241m=\u001b[39m age_metallicity\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lookup_interpolation(age, metallicity)\n\u001b[0;32m----> 5\u001b[0m interpolation \u001b[38;5;241m=\u001b[39m \u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlookup_interpolation_lax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetallicity\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", + " \u001b[0;31m[... skipping hidden 14 frame]\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/rubix/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py:1298\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_token_bufs(result_token_bufs, sharded_runtime_token)\n\u001b[1;32m 1297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1298\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dispatch\u001b[38;5;241m.\u001b[39mneeds_check_special():\n\u001b[1;32m 1301\u001b[0m out_arrays \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mdisassemble_into_single_device_arrays()\n", + "\u001b[0;31mXlaRuntimeError\u001b[0m: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes.: while running replica 0 and partition 0 of a replicated computation (other replicas may have failed as well)." + ] + } + ], + "source": [ + "def lookup_interpolation_lax(age_metallicity):\n", + " age, metallicity = age_metallicity\n", + " return lookup_interpolation(age, metallicity)\n", + "\n", + "interpolation = jax.lax.map(lookup_interpolation_lax, (age, metallicity), batch_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "47236b6c", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'lookup_interpolation_lax' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m _, interpolation \u001b[38;5;241m=\u001b[39m \u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlookup_interpolation_lax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetallicity\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + " \u001b[0;31m[... skipping hidden 10 frame]\u001b[0m\n", + "Cell \u001b[0;32mIn[4], line 2\u001b[0m, in \u001b[0;36m\u001b[0;34m(carry, x)\u001b[0m\n\u001b[1;32m 1\u001b[0m _, interpolation \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mlax\u001b[38;5;241m.\u001b[39mscan(\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m carry, x: (carry, \u001b[43mlookup_interpolation_lax\u001b[49m(x)),\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4\u001b[0m (age, metallicity),\n\u001b[1;32m 5\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'lookup_interpolation_lax' is not defined" + ] + } + ], + "source": [ + "_, interpolation = jax.lax.scan(\n", + " lambda carry, x: (carry, lookup_interpolation_lax(x)),\n", + " None,\n", + " (age, metallicity),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef96f19f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94e29597", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[CudaDevice(id=0), CudaDevice(id=1)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b0c1c99", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/rubix_pipeline_sharding.py b/notebooks/rubix_pipeline_sharding.py index e69de29b..b8504d94 100644 --- a/notebooks/rubix_pipeline_sharding.py +++ b/notebooks/rubix_pipeline_sharding.py @@ -0,0 +1,123 @@ +import os + +#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3' + +# Specify the number of GPUs to use +os.environ['CUDA_VISIBLE_DEVICES'] = "1,4,5,8,9" + +os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false" + +#Set the FSPS path to the template files +# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps' +#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps' +#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps' +os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps' + +import jax +import jax.numpy as jnp +import matplotlib.pyplot as plt +from rubix.core.pipeline import RubixPipeline +# Now JAX will list two CpuDevice entries +print(jax.devices()) + + + +config = { + "pipeline":{"name": "calc_ifu"}, + + "logger": { + "log_level": "DEBUG", + "log_file_path": None, + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + "data": { + "name": "IllustrisAPI", + "args": { + "api_key": os.environ.get("ILLUSTRIS_API_KEY"), + "particle_type": ["stars"], + "simulation": "TNG50-1", + "snapshot": 99, + "save_data_path": "data", + }, + + "load_galaxy_args": { + "id": 14, + "reuse": True, + }, + + "subset": { + "use_subset": True, + "subset_size": 10000, + }, + }, + "simulation": { + "name": "IllustrisTNG", + "args": { + "path": "data/galaxy-id-14.hdf5", + }, + + }, + "output_path": "output", + + "telescope": + {"name": "MUSE", + "psf": {"name": "gaussian", "size": 5, "sigma": 0.6}, + "lsf": {"sigma": 0.5}, + "noise": {"signal_to_noise": 100,"noise_distribution": "normal"},}, + "cosmology": + {"name": "PLANCK15"}, + + "galaxy": + {"dist_z": 0.1, + "rotation": {"type": "edge-on"}, + }, + + "ssp": { + "template": { + "name": "FSPS" + }, + "dust": { + "extinction_model": "Cardelli89", + "dust_to_gas_ratio": 0.01, + "dust_to_metals_ratio": 0.4, + "dust_grain_density": 3.5, + "Rv": 3.1, + }, + }, +} + +pipe = RubixPipeline(config) +inputdata = pipe.prepare_data() +rubixdata = pipe.run_sharded(inputdata) + + +#Plotting the spectra +wave = pipe.telescope.wave_seq + +plt.figure(figsize=(10, 5)) +plt.title("Spectra of a single star") +plt.xlabel("Wavelength (Angstroms)") +plt.ylabel("Luminosity") +#spectra = rubixdata.stars.datacube # Spectra of all stars +spectra = rubixdata +plt.plot(wave, spectra[12,12,:]) +plt.plot(wave, spectra[12,14,:]) +plt.savefig("./output/rubix_spectra.jpg") +plt.close() + +plt.figure(figsize=(6, 5)) +# get the indices of the visible wavelengths of 4000-8000 Angstroms +visible_indices = jnp.where((wave >= 4000) & (wave <= 8000)) +#visible_spectra = rubixdata.stars.datacube[:, :, visible_indices[0]] +visible_spectra = rubixdata[:, :, visible_indices[0]] +# Sum up all spectra to create an image +image = jnp.sum(visible_spectra, axis = 2) +plt.imshow(image, origin="lower", cmap="inferno") +plt.colorbar() +plt.title("Image of the galaxy") +plt.xlabel("X pixel") +plt.ylabel("Y pixel") +plt.savefig("./output/rubix_image.jpg") +plt.close() + + diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 54d6e814..bc11147c 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -29,7 +29,7 @@ "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '7, 8, 9'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", "\n", "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", @@ -104,7 +104,24 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-09 04:50:00,698 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-05-09 04:50:00,699 - rubix - INFO - Rubix version: 0.0.post417+g76e9abf.d20250424\n", + "2025-05-09 04:50:00,699 - rubix - INFO - JAX version: 0.6.0\n", + "2025-05-09 04:50:00,700 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -135,7 +152,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 30000,\n", + " \"subset_size\": 20000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -332,7 +349,7 @@ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", "\n", - "spectra = rubixdata#.stars.datacube # Spectra of all stars\n", + "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", "spectra_sharded = shard_rubixdata # Spectra of all stars\n", "#print(spectra.shape)\n", "\n", @@ -341,8 +358,8 @@ "plt.title(\"Rubix\")\n", "plt.xlabel(\"Wavelength [Angstrom]\")\n", "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "plt.plot(wave, spectra[12,12,:])\n", - "plt.plot(wave, spectra[8,12,:])\n", + "#plt.plot(wave, spectra[12,12,:])\n", + "#plt.plot(wave, spectra[8,12,:])\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.title(\"Rubix Sharded\")\n", @@ -370,20 +387,20 @@ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", - "visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", "sharded_visible_spectra = shard_rubixdata[ :, :, visible_indices[0]]\n", "#visible_spectra.shape\n", "\n", - "image = jnp.sum(visible_spectra, axis=2)\n", + "#image = jnp.sum(visible_spectra, axis=2)\n", "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", "\n", "# Plot side by side\n", "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", "\n", "# Original IFU datacube image\n", - "im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", + "#im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", "axes[0].set_title(\"Original IFU Datacube\")\n", - "fig.colorbar(im0, ax=axes[0])\n", + "#fig.colorbar(im0, ax=axes[0])\n", "\n", "# Sharded IFU datacube image\n", "im1 = axes[1].imshow(sharded_image, origin=\"lower\", cmap=\"inferno\")\n", diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 16130811..c43772c6 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -55,9 +55,13 @@ def get_calculate_spectra(config: dict) -> Callable: >>> rubixdata.stars.spectra """ logger = get_logger(config.get("logger", None)) - lookup_interpolation_pmap = get_lookup_interpolation_pmap(config) - # lookup_interpolation_vmap = get_lookup_interpolation_vmap(config) + #lookup_interpolation_pmap = get_lookup_interpolation_pmap(config) + #lookup_interpolation_vmap = get_lookup_interpolation_vmap(config) lookup_interpolation = get_lookup_interpolation(config) + + def lookup_interpolation_laxmap(age_metallicity): + age, metallicity = age_metallicity + return lookup_interpolation(metallicity, age) @jaxtyped(typechecker=typechecker) def calculate_spectra(rubixdata: RubixData) -> RubixData: @@ -99,6 +103,11 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: metallicity, age, ) + #spectra = jax.lax.map( + # lookup_interpolation_laxmap, + # (metallicity, age), + # batch_size=2, + #) logger.debug(f"Calculation Finished! Spectra shape: {spectra.shape}") spectra_jax = jnp.array(spectra) #spectra_jax = jnp.expand_dims(spectra_jax, axis=0) diff --git a/rubix/core/ssp.py b/rubix/core/ssp.py index 511dd8ca..ca5a6e65 100644 --- a/rubix/core/ssp.py +++ b/rubix/core/ssp.py @@ -79,6 +79,7 @@ def get_lookup_interpolation_vmap(config: dict) -> Callable: """ lookup = get_lookup_interpolation(config) lookup_vmap = jax.vmap(lookup, in_axes=(0, 0)) + return lookup_vmap From 6c1133a42e2f9bea1625bb662f5590a14a5329ff Mon Sep 17 00:00:00 2001 From: anschaible Date: Mon, 19 May 2025 12:49:02 +0000 Subject: [PATCH 20/76] test --- notebooks/rubix_pipeline_single_function_shard_map.ipynb | 2 ++ 1 file changed, 2 insertions(+) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index bc11147c..a7c9e298 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -6,6 +6,7 @@ "metadata": {}, "outputs": [], "source": [ + "#NBVAL_SKIP\n", "import os\n", "import multiprocessing\n", "\n", @@ -23,6 +24,7 @@ "metadata": {}, "outputs": [], "source": [ + "#NBVAL_SKIP\n", "import os\n", "\n", "# Tell XLA to fake 2 host CPU devices\n", From 9938581ee1962b3aaa45ea805752987f36ed628a Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 20 May 2025 00:59:49 +0200 Subject: [PATCH 21/76] test --- notebooks/rubix_pipeline_sharding.py | 7 +- ...x_pipeline_single_function_shard_map.ipynb | 155 ++++++++++++++---- rubix/spectra/ssp/fsps_grid.py | 3 +- 3 files changed, 128 insertions(+), 37 deletions(-) diff --git a/notebooks/rubix_pipeline_sharding.py b/notebooks/rubix_pipeline_sharding.py index b8504d94..b9734973 100644 --- a/notebooks/rubix_pipeline_sharding.py +++ b/notebooks/rubix_pipeline_sharding.py @@ -3,15 +3,16 @@ #os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3' # Specify the number of GPUs to use -os.environ['CUDA_VISIBLE_DEVICES'] = "1,4,5,8,9" +#os.environ['CUDA_VISIBLE_DEVICES'] = "1,4,5,8,9" -os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false" +#os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false" #Set the FSPS path to the template files # os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps' #os.environ['SPS_HOME'] = '/home/annalena/sps_fsps' #os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps' -os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps' +#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps' +os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps' import jax import jax.numpy as jnp diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index a7c9e298..c15edaec 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,38 +2,46 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#NBVAL_SKIP\n", - "import os\n", - "import multiprocessing\n", + "#import os\n", + "#import multiprocessing\n", "\n", "# Logical cores (includes hyperthreads)\n", - "print(\"Logical cores:\", os.cpu_count())\n", + "#print(\"Logical cores:\", os.cpu_count())\n", "\n", "\n", "# Total threads/cores via multiprocessing\n", - "print(\"multiprocessing.cpu_count():\", multiprocessing.cpu_count())\n" + "#print(\"multiprocessing.cpu_count():\", multiprocessing.cpu_count())\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0)]\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import os\n", "\n", "# Tell XLA to fake 2 host CPU devices\n", - "os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", + "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", + "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", "\n", - "os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", + "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", "import jax\n", "\n", @@ -44,16 +52,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# NBVAL_SKIP\n", - "import os\n", + "#import os\n", "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'" + "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", + "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" ] }, { @@ -111,16 +120,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-05-09 04:50:00,698 - rubix - INFO - \n", + "2025-05-20 00:53:58,830 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-05-09 04:50:00,699 - rubix - INFO - Rubix version: 0.0.post417+g76e9abf.d20250424\n", - "2025-05-09 04:50:00,699 - rubix - INFO - JAX version: 0.6.0\n", - "2025-05-09 04:50:00,700 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)] devices\n" + "2025-05-20 00:53:58,831 - rubix - INFO - Rubix version: 0.0.post427+g6c1133a\n", + "2025-05-20 00:53:58,832 - rubix - INFO - JAX version: 0.6.0\n", + "2025-05-20 00:53:58,832 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" ] } ], @@ -280,9 +289,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config)" @@ -290,9 +308,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-20 00:53:59,182 - rubix - INFO - Getting rubix data...\n", + "2025-05-20 00:53:59,183 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-05-20 00:53:59,227 - rubix - INFO - Centering stars particles\n", + "2025-05-20 00:53:59,857 - rubix - WARNING - The Subset value is set in config. Using only subset of size 20000 for stars\n", + "2025-05-20 00:53:59,859 - rubix - INFO - Data loaded with 20000 star particles and 0 gas particles.\n", + "2025-05-20 00:53:59,859 - rubix - INFO - Setting up the pipeline...\n", + "2025-05-20 00:53:59,860 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-05-20 00:53:59,861 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-05-20 00:53:59,864 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 00:53:59,885 - rubix - INFO - Getting cosmology...\n", + "2025-05-20 00:54:00,051 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-05-20 00:54:00,062 - rubix - INFO - Getting cosmology...\n", + "2025-05-20 00:54:00,109 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 00:54:00,186 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 00:54:00,204 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 00:54:00,346 - rubix - INFO - Assembling the pipeline...\n", + "2025-05-20 00:54:00,347 - rubix - INFO - Compiling the expressions...\n", + "2025-05-20 00:54:00,348 - rubix - INFO - Number of devices: 1\n", + "2025-05-20 00:54:00,426 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-05-20 00:54:00,534 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-05-20 00:54:00,539 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-05-20 00:54:00,565 - rubix - INFO - Calculating IFU cube...\n", + "2025-05-20 00:54:00,566 - rubix - DEBUG - Input shapes: Metallicity: 20000, Age: 20000\n", + "2025-05-20 00:54:00,708 - rubix - DEBUG - Calculation Finished! Spectra shape: (20000, 5994)\n", + "2025-05-20 00:54:00,709 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-05-20 00:54:00,714 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-05-20 00:54:00,714 - rubix - DEBUG - Doppler Shifted SSP Wave: (20000, 5994)\n", + "2025-05-20 00:54:00,715 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-05-20 00:54:00,801 - rubix - INFO - Calculating Data Cube...\n", + "2025-05-20 00:54:00,803 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-05-20 00:54:00,804 - rubix - INFO - Convolving with PSF...\n", + "2025-05-20 00:54:00,807 - rubix - INFO - Convolving with LSF...\n", + "2025-05-20 00:54:00,812 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-05-20 00:54:08,107 - rubix - INFO - Pipeline run completed in 8.25 seconds.\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "\n", @@ -302,14 +369,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", - "inputdata = pipe.prepare_data()\n", - "shard_rubixdata = pipe.run_sharded_chunked(inputdata)" + "#inputdata = pipe.prepare_data()\n", + "#shard_rubixdata = pipe.run_sharded_chunked(inputdata)" ] }, { @@ -323,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -344,15 +411,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", "\n", "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", - "spectra_sharded = shard_rubixdata # Spectra of all stars\n", + "spectra_sharded = rubixdata # Spectra of all stars\n", "#print(spectra.shape)\n", "\n", "plt.figure(figsize=(10, 5))\n", @@ -382,15 +460,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", - "sharded_visible_spectra = shard_rubixdata[ :, :, visible_indices[0]]\n", + "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", "#visible_spectra.shape\n", "\n", "#image = jnp.sum(visible_spectra, axis=2)\n", @@ -425,7 +514,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rubix", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -439,7 +528,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/rubix/spectra/ssp/fsps_grid.py b/rubix/spectra/ssp/fsps_grid.py index 4dae3c83..555343c7 100644 --- a/rubix/spectra/ssp/fsps_grid.py +++ b/rubix/spectra/ssp/fsps_grid.py @@ -108,9 +108,10 @@ def retrieve_ssp_data_from_fsps( _wave, _fluxes = sp.get_spectrum(zmet=zmet, tage=tage, peraa=peraa) spectrum_collector.append(_fluxes) ssp_wave = np.array(_wave) + ssp_wave_centered = ssp_wave - 1.5 ssp_flux = np.array(spectrum_collector) - grid = SSPGrid(ssp_lg_age_gyr, ssp_lgmet, ssp_wave, ssp_flux) + grid = SSPGrid(ssp_lg_age_gyr, ssp_lgmet, ssp_wave_centered, ssp_flux) grid.__class__.__name__ = config["name"] return grid From 6020fefd170fe770c04181f5f3615fae96f31eaa Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 20 May 2025 21:16:01 +0200 Subject: [PATCH 22/76] sharding works now, I assume --- .../rubix_pipeline_single_function.ipynb | 185 ++++++++++++++-- ...x_pipeline_single_function_shard_map.ipynb | 203 +++++++++++++----- rubix/core/pipeline.py | 4 + 3 files changed, 329 insertions(+), 63 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function.ipynb b/notebooks/rubix_pipeline_single_function.ipynb index f58971da..9832ed3b 100644 --- a/notebooks/rubix_pipeline_single_function.ipynb +++ b/notebooks/rubix_pipeline_single_function.ipynb @@ -1,5 +1,17 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "import os\n", + "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -48,9 +60,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "'module' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m#NBVAL_SKIP\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RubixPipeline \n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m 5\u001b[39m config = {\n\u001b[32m 6\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mpipeline\u001b[39m\u001b[33m\"\u001b[39m:{\u001b[33m\"\u001b[39m\u001b[33mname\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mcalc_ifu\u001b[39m\u001b[33m\"\u001b[39m},\n\u001b[32m 7\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 66\u001b[39m }, \n\u001b[32m 67\u001b[39m }\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/core/pipeline.py:28\u001b[39m\n\u001b[32m 25\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m linear_pipeline \u001b[38;5;28;01mas\u001b[39;00m pipeline\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_config, get_pipeline_config\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_reshape_data, get_rubix_data\n\u001b[32m 29\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdust\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_extinction\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mifu\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 31\u001b[39m get_calculate_datacube,\n\u001b[32m 32\u001b[39m get_calculate_spectra,\n\u001b[32m 33\u001b[39m get_doppler_shift_and_resampling,\n\u001b[32m 34\u001b[39m get_scale_spectrum_by_mass,\n\u001b[32m 35\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/core/data.py:13\u001b[39m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mbeartype\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m beartype \u001b[38;5;28;01mas\u001b[39;00m typechecker\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjaxtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m jaxtyped\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgalaxy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m IllustrisAPI, get_input_handler\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgalaxy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01malignment\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m center_particles\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mlogger\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_logger\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/galaxy/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01minput_handler\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 2\u001b[39m IllustrisHandler,\n\u001b[32m 3\u001b[39m BaseHandler,\n\u001b[32m 4\u001b[39m IllustrisAPI,\n\u001b[32m 5\u001b[39m get_input_handler,\n\u001b[32m 6\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/galaxy/input_handler/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01millustris\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m IllustrisHandler\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseHandler\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01millustris_api\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m IllustrisAPI\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/galaxy/input_handler/illustris.py:9\u001b[39m\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m convert_values_to_physical, SFTtoAge\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[38;5;28;43;01mclass\u001b[39;49;00m\u001b[38;5;250;43m \u001b[39;49m\u001b[34;43;01mIllustrisHandler\u001b[39;49;00m\u001b[43m(\u001b[49m\u001b[43mBaseHandler\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 10\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 11\u001b[39m \u001b[33;43;03m This class is used to handle the input data from the Illustris simulation.\u001b[39;49;00m\n\u001b[32m 12\u001b[39m \u001b[33;43;03m The data is stored in HDF5 files, which are read using the h5py library.\u001b[39;49;00m\n\u001b[32m 13\u001b[39m \u001b[33;43;03m The data is then converted to physical units using the values in the header of the file.\u001b[39;49;00m\n\u001b[32m 14\u001b[39m \u001b[33;43;03m The data is then stored in a dictionary, which can be accessed using the get_particle_data() method.\u001b[39;49;00m\n\u001b[32m 15\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\n\u001b[32m 17\u001b[39m \u001b[43m \u001b[49m\u001b[43mMAPPED_FIELDS\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mIllustrisHandler\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mMAPPED_FIELDS\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/galaxy/input_handler/illustris.py:17\u001b[39m, in \u001b[36mIllustrisHandler\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mIllustrisHandler\u001b[39;00m(BaseHandler):\n\u001b[32m 10\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 11\u001b[39m \u001b[33;03m This class is used to handle the input data from the Illustris simulation.\u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[33;03m The data is stored in HDF5 files, which are read using the h5py library.\u001b[39;00m\n\u001b[32m 13\u001b[39m \u001b[33;03m The data is then converted to physical units using the values in the header of the file.\u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[33;03m The data is then stored in a dictionary, which can be accessed using the get_particle_data() method.\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m MAPPED_FIELDS = \u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mIllustrisHandler\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mMAPPED_FIELDS\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# This Dictionary maps the particle name in the simulation to the name used in Rubix\u001b[39;00m\n\u001b[32m 19\u001b[39m MAPPED_PARTICLE_KEYS = config[\u001b[33m\"\u001b[39m\u001b[33mIllustrisHandler\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mMAPPED_PARTICLE_KEYS\u001b[39m\u001b[33m\"\u001b[39m]\n", + "\u001b[31mTypeError\u001b[39m: 'module' object is not subscriptable" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -81,7 +111,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 1000,\n", + " \"subset_size\": 100000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -97,7 +127,7 @@ " {\"name\": \"MUSE\",\n", " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 1,\"noise_distribution\": \"normal\"},},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", " \"cosmology\":\n", " {\"name\": \"PLANCK15\"},\n", " \n", @@ -108,7 +138,7 @@ " \n", " \"ssp\": {\n", " \"template\": {\n", - " \"name\": \"FSPS\"\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", " },\n", " \"dust\": {\n", " \"extinction_model\": \"Cardelli89\",\n", @@ -209,7 +239,65 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 20:56:48,004 - rubix - INFO - Getting rubix data...\n", + "2025-05-20 20:56:48,005 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-05-20 20:56:48,084 - rubix - INFO - Centering stars particles\n", + "2025-05-20 20:56:49,283 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100000 for stars\n", + "2025-05-20 20:56:49,284 - rubix - INFO - Data loaded with 100000 star particles and 0 gas particles.\n", + "2025-05-20 20:56:49,285 - rubix - INFO - Setting up the pipeline...\n", + "2025-05-20 20:56:49,285 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'reshape_data': {'name': 'reshape_data', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'reshape_data', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-05-20 20:56:49,286 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-05-20 20:56:49,288 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 20:56:49,299 - rubix - INFO - Getting cosmology...\n", + "2025-05-20 20:56:49,440 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-05-20 20:56:49,450 - rubix - INFO - Getting cosmology...\n", + "2025-05-20 20:56:49,476 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-05-20 20:56:49,522 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 20:56:49,579 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 20:56:49,592 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 20:56:49,792 - rubix - INFO - Assembling the pipeline...\n", + "2025-05-20 20:56:49,793 - rubix - INFO - Compiling the expressions...\n", + "2025-05-20 20:56:49,794 - rubix - INFO - Running the pipeline on the input data...\n", + "2025-05-20 20:56:49,795 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-05-20 20:56:49,796 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-05-20 20:56:49,796 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-05-20 20:56:49,856 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-05-20 20:56:49,861 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-05-20 20:56:49,878 - rubix - WARNING - Attribute value of datacube is None or not an array\n", + "2025-05-20 20:56:49,882 - rubix - WARNING - Attribute value of spectra is None or not an array\n", + "2025-05-20 20:56:49,883 - rubix - WARNING - Attribute value of tree_flatten is None or not an array\n", + "2025-05-20 20:56:49,883 - rubix - WARNING - Attribute value of tree_unflatten is None or not an array\n", + "2025-05-20 20:56:49,884 - rubix - INFO - Calculating IFU cube...\n", + "2025-05-20 20:56:49,884 - rubix - DEBUG - Input shapes: Metallicity: 1, Age: 1\n", + "2025-05-20 20:56:49,989 - rubix - DEBUG - Calculation Finished! Spectra shape: (100000, 5994)\n", + "2025-05-20 20:56:49,991 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-05-20 20:56:49,998 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-05-20 20:56:49,999 - rubix - DEBUG - Doppler Shifted SSP Wave: (1, 100000, 5994)\n", + "2025-05-20 20:56:49,999 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-05-20 20:56:50,312 - rubix - INFO - Calculating Data Cube...\n", + "2025-05-20 20:56:50,315 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-05-20 20:56:50,316 - rubix - INFO - Convolving with PSF...\n", + "2025-05-20 20:56:50,320 - rubix - INFO - Convolving with LSF...\n", + "2025-05-20 20:56:50,326 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-05-20 20:57:19,641 - rubix - INFO - Pipeline run completed in 30.36 seconds.\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config)\n", @@ -217,6 +305,27 @@ "rubixdata = pipe.run()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'RubixPipeline' object has no attribute 'run_sharded'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m rubixdata_2 = \u001b[43mpipe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_sharded\u001b[49m()\n", + "\u001b[31mAttributeError\u001b[39m: 'RubixPipeline' object has no attribute 'run_sharded'" + ] + } + ], + "source": [ + "rubixdata_2 = pipe.run_sharded()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -251,7 +360,35 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(25, 25, 3721)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -259,7 +396,10 @@ "spectra = rubixdata.stars.datacube # Spectra of all stars\n", "print(spectra.shape)\n", "\n", - "plt.plot(wave, spectra[12,12,:])\n" + "plt.plot(wave, spectra[12,12,:])\n", + "plt.plot(wave, spectra[14,12,:])\n", + "plt.plot(wave, spectra[6,9,:])\n", + "plt.plot(wave, spectra[9,6,:])" ] }, { @@ -273,7 +413,28 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", @@ -282,7 +443,7 @@ "\n", "# Sum up all spectra to create an image\n", "image = jnp.sum(visible_spectra, axis = 2)\n", - "plt.imshow(image, origin=\"lower\", cmap=\"inferno\")\n", + "plt.imshow(image.T, origin=\"lower\", cmap=\"inferno\")\n", "plt.colorbar()" ] }, @@ -298,7 +459,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rubix", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -312,7 +473,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index c15edaec..2cfcea71 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -113,23 +113,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-05-20 00:53:58,830 - rubix - INFO - \n", + "2025-05-20 21:14:00,034 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-05-20 00:53:58,831 - rubix - INFO - Rubix version: 0.0.post427+g6c1133a\n", - "2025-05-20 00:53:58,832 - rubix - INFO - JAX version: 0.6.0\n", - "2025-05-20 00:53:58,832 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" + "2025-05-20 21:14:00,035 - rubix - INFO - Rubix version: 0.0.post428+g9938581.d20250520\n", + "2025-05-20 21:14:00,035 - rubix - INFO - JAX version: 0.6.0\n", + "2025-05-20 21:14:00,036 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" ] } ], @@ -190,7 +190,7 @@ " \n", " \"ssp\": {\n", " \"template\": {\n", - " \"name\": \"FSPS\"\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", " },\n", " \"dust\": {\n", " \"extinction_model\": \"Cardelli89\",\n", @@ -289,14 +289,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n" ] } @@ -308,55 +308,55 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-05-20 00:53:59,182 - rubix - INFO - Getting rubix data...\n", - "2025-05-20 00:53:59,183 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-05-20 00:53:59,227 - rubix - INFO - Centering stars particles\n", - "2025-05-20 00:53:59,857 - rubix - WARNING - The Subset value is set in config. Using only subset of size 20000 for stars\n", - "2025-05-20 00:53:59,859 - rubix - INFO - Data loaded with 20000 star particles and 0 gas particles.\n", - "2025-05-20 00:53:59,859 - rubix - INFO - Setting up the pipeline...\n", - "2025-05-20 00:53:59,860 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-05-20 00:53:59,861 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-05-20 00:53:59,864 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + "2025-05-20 21:14:01,119 - rubix - INFO - Getting rubix data...\n", + "2025-05-20 21:14:01,121 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-05-20 21:14:01,194 - rubix - INFO - Centering stars particles\n", + "2025-05-20 21:14:02,416 - rubix - WARNING - The Subset value is set in config. Using only subset of size 20000 for stars\n", + "2025-05-20 21:14:02,419 - rubix - INFO - Data loaded with 20000 star particles and 0 gas particles.\n", + "2025-05-20 21:14:02,420 - rubix - INFO - Setting up the pipeline...\n", + "2025-05-20 21:14:02,420 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-05-20 21:14:02,422 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-05-20 21:14:02,424 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-05-20 00:53:59,885 - rubix - INFO - Getting cosmology...\n", - "2025-05-20 00:54:00,051 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-05-20 00:54:00,062 - rubix - INFO - Getting cosmology...\n", - "2025-05-20 00:54:00,109 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + "2025-05-20 21:14:02,446 - rubix - INFO - Getting cosmology...\n", + "2025-05-20 21:14:02,607 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-05-20 21:14:02,615 - rubix - INFO - Getting cosmology...\n", + "2025-05-20 21:14:03,062 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-05-20 00:54:00,186 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + "2025-05-20 21:14:03,538 - rubix - DEBUG - SSP Wave: (5333,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-05-20 00:54:00,204 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.11/site-packages/rubix/telescope/telescopes.yaml\n", + "2025-05-20 21:14:03,549 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-05-20 00:54:00,346 - rubix - INFO - Assembling the pipeline...\n", - "2025-05-20 00:54:00,347 - rubix - INFO - Compiling the expressions...\n", - "2025-05-20 00:54:00,348 - rubix - INFO - Number of devices: 1\n", - "2025-05-20 00:54:00,426 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-05-20 00:54:00,534 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-05-20 00:54:00,539 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-05-20 00:54:00,565 - rubix - INFO - Calculating IFU cube...\n", - "2025-05-20 00:54:00,566 - rubix - DEBUG - Input shapes: Metallicity: 20000, Age: 20000\n", - "2025-05-20 00:54:00,708 - rubix - DEBUG - Calculation Finished! Spectra shape: (20000, 5994)\n", - "2025-05-20 00:54:00,709 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-05-20 00:54:00,714 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-05-20 00:54:00,714 - rubix - DEBUG - Doppler Shifted SSP Wave: (20000, 5994)\n", - "2025-05-20 00:54:00,715 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-05-20 00:54:00,801 - rubix - INFO - Calculating Data Cube...\n", - "2025-05-20 00:54:00,803 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-05-20 00:54:00,804 - rubix - INFO - Convolving with PSF...\n", - "2025-05-20 00:54:00,807 - rubix - INFO - Convolving with LSF...\n", - "2025-05-20 00:54:00,812 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-05-20 00:54:08,107 - rubix - INFO - Pipeline run completed in 8.25 seconds.\n" + "2025-05-20 21:14:03,734 - rubix - INFO - Assembling the pipeline...\n", + "2025-05-20 21:14:03,735 - rubix - INFO - Compiling the expressions...\n", + "2025-05-20 21:14:03,736 - rubix - INFO - Number of devices: 1\n", + "2025-05-20 21:14:03,818 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-05-20 21:14:03,924 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-05-20 21:14:03,928 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-05-20 21:14:03,953 - rubix - INFO - Calculating IFU cube...\n", + "2025-05-20 21:14:03,953 - rubix - DEBUG - Input shapes: Metallicity: 20000, Age: 20000\n", + "2025-05-20 21:14:04,177 - rubix - DEBUG - Calculation Finished! Spectra shape: (20000, 5333)\n", + "2025-05-20 21:14:04,178 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-05-20 21:14:04,183 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-05-20 21:14:04,184 - rubix - DEBUG - Doppler Shifted SSP Wave: (20000, 5333)\n", + "2025-05-20 21:14:04,184 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-05-20 21:14:04,253 - rubix - INFO - Calculating Data Cube...\n", + "2025-05-20 21:14:04,255 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-05-20 21:14:04,255 - rubix - INFO - Convolving with PSF...\n", + "2025-05-20 21:14:04,259 - rubix - INFO - Convolving with LSF...\n", + "2025-05-20 21:14:04,264 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-05-20 21:14:06,268 - rubix - INFO - Pipeline run completed in 3.85 seconds.\n" ] } ], @@ -367,6 +367,107 @@ "rubixdata = pipe.run_sharded(inputdata)" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[ 1.8663327e-01 2.0253532e-01 2.0300621e-01 ... 2.4535783e-02\n", + " 2.8690096e-02 3.0187748e-02]\n", + " [ 2.6096473e+00 3.0808454e+00 2.9036274e+00 ... -1.0372877e+00\n", + " -8.2148838e-01 -6.4967668e-01]\n", + " [ 2.9600172e+00 4.7419906e+00 3.3595605e+00 ... -5.6377354e+00\n", + " -4.6541839e+00 -3.9757755e+00]\n", + " ...\n", + " [ 8.0857529e+01 8.7022888e+01 8.3423561e+01 ... 2.3552149e+01\n", + " 2.2909094e+01 2.1220167e+01]\n", + " [ 2.0778951e+02 2.2062862e+02 2.1599808e+02 ... 6.6492391e+00\n", + " 6.4269590e+00 5.9624753e+00]\n", + " [ 5.6667694e+01 6.0071331e+01 5.9009029e+01 ... 9.3002629e-01\n", + " 9.1504198e-01 8.6860436e-01]]\n", + "\n", + " [[ 8.8623657e+00 9.4128408e+00 9.5636234e+00 ... 2.6624138e+00\n", + " 2.7262561e+00 2.6511188e+00]\n", + " [ 3.9778210e+01 4.2250408e+01 4.2992622e+01 ... 1.0783029e+01\n", + " 1.1080705e+01 1.0803394e+01]\n", + " [-4.1083000e+01 -4.1158161e+01 -4.3862789e+01 ... -9.1105967e+00\n", + " -8.0653925e+00 -7.5847492e+00]\n", + " ...\n", + " [ 3.6464722e+02 3.8163013e+02 3.7437909e+02 ... 4.9800919e+01\n", + " 4.9048306e+01 4.6190613e+01]\n", + " [ 9.7805939e+02 1.0347834e+03 1.0180806e+03 ... 4.0949604e+01\n", + " 3.9178234e+01 3.5780865e+01]\n", + " [ 5.5939709e+02 5.9595532e+02 5.9040039e+02 ... 2.0157406e+01\n", + " 2.0081875e+01 1.8848007e+01]]\n", + "\n", + " [[-3.1875231e+01 -3.2292850e+01 -3.5248451e+01 ... 3.8041861e+00\n", + " 4.3648586e+00 4.3608699e+00]\n", + " [ 1.4530573e+02 1.5422511e+02 1.5667447e+02 ... 4.3676464e+01\n", + " 4.4651451e+01 4.3363514e+01]\n", + " [ 3.8403248e+01 4.0862396e+01 4.1102512e+01 ... 1.1623282e+01\n", + " 1.1888708e+01 1.1392076e+01]\n", + " ...\n", + " [ 3.1581308e+02 3.2944159e+02 3.2612085e+02 ... 4.9054817e+01\n", + " 4.7293476e+01 4.3396538e+01]\n", + " [ 8.4468103e+02 8.8660309e+02 8.8106769e+02 ... 1.4876683e+02\n", + " 1.4249185e+02 1.2965173e+02]\n", + " [ 1.5732024e+03 1.6637032e+03 1.6499910e+03 ... 1.8444798e+02\n", + " 1.8403534e+02 1.7488028e+02]]\n", + "\n", + " ...\n", + "\n", + " [[ 4.7191711e+01 4.9600300e+01 5.0802711e+01 ... 2.1733906e+01\n", + " 2.1687683e+01 2.0896967e+01]\n", + " [-7.5807352e+00 -8.7038908e+00 -8.5768757e+00 ... 4.4337039e+00\n", + " 4.4951138e+00 4.3524756e+00]\n", + " [-1.1798183e+02 -1.2672647e+02 -1.3065874e+02 ... -1.0312170e+01\n", + " -1.0306068e+01 -1.0045095e+01]\n", + " ...\n", + " [ 6.2256789e+00 6.5035510e+00 6.5278206e+00 ... 1.7979906e+00\n", + " 1.7393030e+00 1.6026521e+00]\n", + " [ 1.9471159e-02 2.2878395e-02 2.7606528e-02 ... 1.0845361e-02\n", + " 1.0766010e-02 1.0458607e-02]\n", + " [ 1.3420188e-01 1.3969469e-01 1.3897365e-01 ... 2.7665328e-02\n", + " 2.7612306e-02 2.6393568e-02]]\n", + "\n", + " [[-1.2728276e+00 -6.6365510e-01 -7.8320765e-01 ... 1.4691648e+00\n", + " 1.8885735e+00 2.0433004e+00]\n", + " [-7.2478516e+01 -7.9048363e+01 -7.9668007e+01 ... -4.5002127e+00\n", + " -4.1424303e+00 -3.8698430e+00]\n", + " [-2.8256409e+02 -3.1128177e+02 -3.1672745e+02 ... -3.2594616e+01\n", + " -3.2962536e+01 -3.2516556e+01]\n", + " ...\n", + " [ 6.7891198e-01 7.6051378e-01 7.0264876e-01 ... 2.8321558e-01\n", + " 2.8089610e-01 2.6009002e-01]\n", + " [ 2.8981071e-02 3.0213954e-02 3.0309886e-02 ... 7.3219240e-03\n", + " 7.1133194e-03 6.5948148e-03]\n", + " [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00]]\n", + "\n", + " [[-4.6797344e+01 -4.6865349e+01 -4.9156483e+01 ... -1.6389694e+01\n", + " -1.4941079e+01 -1.3716887e+01]\n", + " [-2.9661310e+01 -3.1354326e+01 -3.2072586e+01 ... -5.3008184e+00\n", + " -4.8764310e+00 -4.5128131e+00]\n", + " [-7.0464943e+01 -7.7622787e+01 -7.8981514e+01 ... -8.1995840e+00\n", + " -8.2829370e+00 -8.1659317e+00]\n", + " ...\n", + " [-2.8246093e-01 -2.8112534e-01 -2.9653305e-01 ... -4.1474421e-02\n", + " -3.8904652e-02 -3.6205065e-02]\n", + " [ 4.5176136e-04 4.7099241e-04 4.7243867e-04 ... 1.1338825e-04\n", + " 1.1020008e-04 1.0221790e-04]\n", + " [ 0.0000000e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00\n", + " 0.0000000e+00 0.0000000e+00]]]\n" + ] + } + ], + "source": [ + "print(rubixdata)" + ] + }, { "cell_type": "code", "execution_count": 8, @@ -416,7 +517,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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mSbrjjjs0aNAgPfXUU7rwwgu1YMECffnll3rxxRftfSYAAAAAAACQVI2Ez8iRI/XLL79o6tSpys7OVs+ePbVixQr3wMy7du3yKIHu37+/Xn/9dd1333269957dcopp2jZsmXq2rVrlY/pdDo1bdq0Crt5ofbhetU9XLO6hetV93DN6hau17FxfuzHObUf59RenE/7cU7hDw5zos45CgAAAAAAEKROzNEIAQAAAAAAghgJHwAAAAAAgCBDwgcAAAAAACDIkPABAAAAAAAIMrUm4TNr1iy1bdtW4eHhSkxM1Oeff37M7RcvXqzOnTsrPDxc3bp107vvvuunlkLy7XrNnTtXAwcOVNOmTdW0aVMlJSUd9/rCfr6+x8osWLBADodDI0aMqNkGwoOv1+vAgQNKSUlRXFycnE6nOnbsyO9FP/P1ms2cOVOdOnVSRESEEhISdOedd6qgoMBPrT2xrVq1SsOHD1d8fLwcDoeWLVt23H0yMjJ0xhlnyOl06uSTT9b8+fNrvJ21VXU/T1DeAw88IIfD4bF07tw50M2qM473XjbGaOrUqYqLi1NERISSkpL0ww8/BKaxdcTxzun48ePLvWbPP//8wDS2DkhLS1OfPn3UuHFjRUdHa8SIEdqyZYvHNgUFBUpJSVHz5s3VqFEjXX755crJyQlQixFsakXCZ+HChUpNTdW0adP01VdfqUePHkpOTtbevXsr3H716tUaNWqUrrvuOn399dcaMWKERowYoY0bN/q55ScmX69XRkaGRo0apY8++kiZmZlKSEjQ0KFDtWfPHj+3/MTl6zUrs2PHDt11110aOHCgn1oKyffrVVRUpPPOO087duzQkiVLtGXLFs2dO1etWrXyc8tPXL5es9dff12TJ0/WtGnT9N133+mll17SwoULde+99/q55Sem/Px89ejRQ7NmzarS9llZWbrwwgs1ZMgQrVu3TpMmTdL111+v999/v4ZbWvtU9/MElevSpYt+/vln9/LJJ58Eukl1xvHey48//rieffZZzZkzR5999pkaNmyo5ORkkuvHUJXfj+eff77Ha/aNN97wYwvrlo8//lgpKSlas2aNPvjgAx09elRDhw5Vfn6+e5s777xTb731lhYvXqyPP/5YP/30ky677LIAthpBxdQCffv2NSkpKe7HJSUlJj4+3qSlpVW4/VVXXWUuvPBCj3WJiYnmpptuqtF2opSv18tbcXGxady4sXnllVdqqonwUp1rVlxcbPr372/++c9/mnHjxplLLrnEDy2FMb5fr9mzZ5v27duboqIifzURXny9ZikpKeacc87xWJeammoGDBhQo+1EeZLM0qVLj7nN3Xffbbp06eKxbuTIkSY5ObkGW1Y7Wb0HgKdp06aZHj16BLoZQcH7vexyuUxsbKx54okn3OsOHDhgnE6neeONNwLQwrqnot+P3BNas3fvXiPJfPzxx8aY0tdk/fr1zeLFi93bfPfdd0aSyczMDFQzEUQCXuFTVFSktWvXKikpyb0uJCRESUlJyszMrHCfzMxMj+0lKTk5udLtYZ/qXC9vhw8f1tGjR9WsWbOaaib+pLrX7MEHH1R0dLSuu+46fzQTv6vO9Vq+fLn69eunlJQUxcTEqGvXrnrkkUdUUlLir2af0Kpzzfr376+1a9e6u8Js375d7777ri644AK/tBm+4b6jlB33ACjvhx9+UHx8vNq3b6/Ro0dr165dgW5SUMjKylJ2drbH6zUqKkqJiYm8Xi3KyMhQdHS0OnXqpFtuuUX79+8PdJPqjNzcXEly/x20du1aHT161ON12rlzZ7Vu3ZrXKWxRL9AN2Ldvn0pKShQTE+OxPiYmRps3b65wn+zs7Aq3z87OrrF2olR1rpe3e+65R/Hx8eVunlEzqnPNPvnkE7300ktat26dH1qIP6vO9dq+fbtWrlyp0aNH691339XWrVt166236ujRo5o2bZo/mn1Cq841u+aaa7Rv3z6dddZZMsaouLhYN998M126aqnK7jvy8vJ05MgRRUREBKhl/mXHPQA8JSYmav78+erUqZN+/vlnTZ8+XQMHDtTGjRvVuHHjQDevTiv7u4C/Gex1/vnn67LLLlO7du20bds23XvvvRo2bJgyMzMVGhoa6ObVai6XS5MmTdKAAQPUtWtXSaWv07CwMDVp0sRjW16nsEvAEz44sTz66KNasGCBMjIyFB4eHujmoAIHDx7UmDFjNHfuXLVo0SLQzUEVuFwuRUdH68UXX1RoaKh69eqlPXv26IknniDhU0tlZGTokUce0QsvvKDExERt3bpVd9xxhx566CHdf//9gW4eAD8ZNmyY+9/du3dXYmKi2rRpo0WLFlFhi1rp6quvdv+7W7du6t69uzp06KCMjAyde+65AWxZ7ZeSkqKNGzcyThf8KuAJnxYtWig0NLTcSOQ5OTmKjY2tcJ/Y2Fiftod9qnO9yjz55JN69NFH9eGHH6p79+412Uz8ia/XbNu2bdqxY4eGDx/uXudyuSRJ9erV05YtW9ShQ4eabfQJrDrvsbi4ONWvX9/jm7VTTz1V2dnZKioqUlhYWI22+URXnWt2//33a8yYMbr++uslld405+fn68Ybb9Tf/vY3hYQEvMc1/qSy+47IyMgTprpHsnYPgKpp0qSJOnbsqK1btwa6KXVe2WsyJydHcXFx7vU5OTnq2bNngFoVfNq3b68WLVpo69atJHyOYeLEiXr77be1atUqnXTSSe71sbGxKioq0oEDBzyqfPi9CrsE/I4yLCxMvXr1Unp6unudy+VSenq6+vXrV+E+/fr189hekj744INKt4d9qnO9pNJZEh566CGtWLFCvXv39kdT8Ttfr1nnzp21YcMGrVu3zr1cfPHF7tlpEhIS/Nn8E0513mMDBgzQ1q1b3Yk5Sfr+++8VFxdHsscPqnPNDh8+XC6pU5awM8bUXGNRLdx3lKruPQCq7tChQ9q2bZtHggLV065dO8XGxnq8XvPy8vTZZ5/xerXRjz/+qP379/OarYQxRhMnTtTSpUu1cuVKtWvXzuPnvXr1Uv369T1ep1u2bNGuXbt4ncIeAR402hhjzIIFC4zT6TTz5883mzZtMjfeeKNp0qSJyc7ONsYYM2bMGDN58mT39p9++qmpV6+eefLJJ813331npk2bZurXr282bNgQqKdwQvH1ej366KMmLCzMLFmyxPz888/u5eDBg4F6CiccX6+ZN2Zk8C9fr9euXbtM48aNzcSJE82WLVvM22+/baKjo83f//73QD2FE46v12zatGmmcePG5o033jDbt283//3vf02HDh3MVVddFaincEI5ePCg+frrr83XX39tJJkZM2aYr7/+2uzcudMYY8zkyZPNmDFj3Ntv377dNGjQwPz1r3813333nZk1a5YJDQ01K1asCNRTCJjjvdbhm7/85S8mIyPDZGVlmU8//dQkJSWZFi1amL179wa6aXXC8d7Ljz76qGnSpIn5z3/+Y7755htzySWXmHbt2pkjR44EuOW117HO6cGDB81dd91lMjMzTVZWlvnwww/NGWecYU455RRTUFAQ6KbXSrfccouJiooyGRkZHn8HHT582L3NzTffbFq3bm1WrlxpvvzyS9OvXz/Tr1+/ALYawaRWJHyMMea5554zrVu3NmFhYaZv375mzZo17p8NGjTIjBs3zmP7RYsWmY4dO5qwsDDTpUsX88477/i5xSc2X65XmzZtjKRyy7Rp0/zf8BOYr++xPyPh43++Xq/Vq1ebxMRE43Q6Tfv27c3DDz9siouL/dzqE5sv1+zo0aPmgQceMB06dDDh4eEmISHB3Hrrrea3337zf8NPQB999FGFn0tl12jcuHFm0KBB5fbp2bOnCQsLM+3btzfz5s3ze7tri2O91uGbkSNHmri4OBMWFmZatWplRo4cabZu3RroZtUZx3svu1wuc//995uYmBjjdDrNueeea7Zs2RLYRtdyxzqnhw8fNkOHDjUtW7Y09evXN23atDE33HADCd9jqOhcSvL4DDly5Ii59dZbTdOmTU2DBg3MpZdean7++efANRpBxWEMteMAAAAAAADBJOBj+AAAAAAAAMBeJHwAAAAAAACCDAkfAAAAAACAIEPCBwAAAAAAIMiQ8AEAAAAAAAgyJHwAAAAAAACCDAkfAAAAAACAIEPCBwAAAAAAIMiQ8AEAAAAAAAgyJHwAAAAAAACCDAkfAAAAAACAIEPCBwAAAAAAIMj8fzl6wsO0+OBXAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -528,7 +629,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 58db9a15..e61118a3 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -186,6 +186,8 @@ def run_sharded(self, inputdata): It splits the particle arrays (e.g. under stars and gas) into shards, runs the compiled pipeline on each shard, and then combines the resulting datacubes. + This is the recomended method to run the pipeline in parallel at the moment!!! + Parameters ---------- inputdata : object @@ -331,6 +333,8 @@ def run_sharded_chunked(self, inputdata): It splits the particle arrays (e.g. under stars and gas) into shards, runs the compiled pipeline on each shard, and then combines the resulting datacubes. + This is an experimental function and is not recommended to use at the moment!!! + Parameters ---------- inputdata : object From 5b4f76de41b8189dfd0b3a2bad72888c6c08c6b9 Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 20 May 2025 21:39:09 +0200 Subject: [PATCH 23/76] nihao and illustris config --- ...x_pipeline_single_function_shard_map.ipynb | 150 +++++++++++------- 1 file changed, 95 insertions(+), 55 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 2cfcea71..5767206c 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -120,16 +120,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-05-20 21:14:00,034 - rubix - INFO - \n", + "2025-05-20 21:33:34,128 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-05-20 21:14:00,035 - rubix - INFO - Rubix version: 0.0.post428+g9938581.d20250520\n", - "2025-05-20 21:14:00,035 - rubix - INFO - JAX version: 0.6.0\n", - "2025-05-20 21:14:00,036 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" + "2025-05-20 21:33:34,129 - rubix - INFO - Rubix version: 0.0.post429+g6020fef\n", + "2025-05-20 21:33:34,129 - rubix - INFO - JAX version: 0.6.0\n", + "2025-05-20 21:33:34,129 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" ] } ], @@ -138,7 +138,72 @@ "import matplotlib.pyplot as plt\n", "from rubix.core.pipeline import RubixPipeline \n", "import os\n", - "config = {\n", + "\n", + "galaxy_id = \"g8.13e11\"\n", + "\n", + "config_NIHAO = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"NihaoHandler\",\n", + " \"args\": {\n", + " \"particle_type\": [\"stars\", \"gas\"],\n", + " \"save_data_path\": \"data\",\n", + " \"snapshot\": \"1024\",\n", + " },\n", + " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", + " \"subset\": {\"use_subset\": True, \"subset_size\": 100},\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"NIHAO\",\n", + " \"args\": {\n", + " \"path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024',\n", + " \"halo_path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024.z0.000.AHF_halos',\n", + " \"halo_id\": 0,\n", + " },\n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "config_TNG = {\n", " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", " \"logger\": {\n", @@ -289,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -303,60 +368,35 @@ ], "source": [ "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config)" + "pipe = RubixPipeline(config_TNG)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-05-20 21:14:01,119 - rubix - INFO - Getting rubix data...\n", - "2025-05-20 21:14:01,121 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-05-20 21:14:01,194 - rubix - INFO - Centering stars particles\n", - "2025-05-20 21:14:02,416 - rubix - WARNING - The Subset value is set in config. Using only subset of size 20000 for stars\n", - "2025-05-20 21:14:02,419 - rubix - INFO - Data loaded with 20000 star particles and 0 gas particles.\n", - "2025-05-20 21:14:02,420 - rubix - INFO - Setting up the pipeline...\n", - "2025-05-20 21:14:02,420 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-05-20 21:14:02,422 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-05-20 21:14:02,424 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 21:14:02,446 - rubix - INFO - Getting cosmology...\n", - "2025-05-20 21:14:02,607 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-05-20 21:14:02,615 - rubix - INFO - Getting cosmology...\n", - "2025-05-20 21:14:03,062 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 21:14:03,538 - rubix - DEBUG - SSP Wave: (5333,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 21:14:03,549 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 21:14:03,734 - rubix - INFO - Assembling the pipeline...\n", - "2025-05-20 21:14:03,735 - rubix - INFO - Compiling the expressions...\n", - "2025-05-20 21:14:03,736 - rubix - INFO - Number of devices: 1\n", - "2025-05-20 21:14:03,818 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-05-20 21:14:03,924 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-05-20 21:14:03,928 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-05-20 21:14:03,953 - rubix - INFO - Calculating IFU cube...\n", - "2025-05-20 21:14:03,953 - rubix - DEBUG - Input shapes: Metallicity: 20000, Age: 20000\n", - "2025-05-20 21:14:04,177 - rubix - DEBUG - Calculation Finished! Spectra shape: (20000, 5333)\n", - "2025-05-20 21:14:04,178 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-05-20 21:14:04,183 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-05-20 21:14:04,184 - rubix - DEBUG - Doppler Shifted SSP Wave: (20000, 5333)\n", - "2025-05-20 21:14:04,184 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-05-20 21:14:04,253 - rubix - INFO - Calculating Data Cube...\n", - "2025-05-20 21:14:04,255 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-05-20 21:14:04,255 - rubix - INFO - Convolving with PSF...\n", - "2025-05-20 21:14:04,259 - rubix - INFO - Convolving with LSF...\n", - "2025-05-20 21:14:04,264 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-05-20 21:14:06,268 - rubix - INFO - Pipeline run completed in 3.85 seconds.\n" + "2025-05-20 21:33:35,267 - rubix - INFO - Getting rubix data...\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Unknown data source: NihaoHandler.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m#NBVAL_SKIP\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m inputdata = \u001b[43mpipe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprepare_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m rubixdata = pipe.run_sharded(inputdata)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/pipeline.py:76\u001b[39m, in \u001b[36mRubixPipeline.prepare_data\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 69\u001b[39m \u001b[33;03mPrepares and loads the data for the pipeline.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 73\u001b[39m \u001b[33;03m 'coords', 'velocities', 'mass', 'age', and 'metallicity' under stars and gas.\u001b[39;00m\n\u001b[32m 74\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 75\u001b[39m \u001b[38;5;28mself\u001b[39m.logger.info(\u001b[33m\"\u001b[39m\u001b[33mGetting rubix data...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m76\u001b[39m rubixdata = \u001b[43mget_rubix_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43muser_config\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 77\u001b[39m star_count = (\n\u001b[32m 78\u001b[39m \u001b[38;5;28mlen\u001b[39m(rubixdata.stars.coords) \u001b[38;5;28;01mif\u001b[39;00m rubixdata.stars.coords \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[32m0\u001b[39m\n\u001b[32m 79\u001b[39m )\n\u001b[32m 80\u001b[39m gas_count = \u001b[38;5;28mlen\u001b[39m(rubixdata.gas.coords) \u001b[38;5;28;01mif\u001b[39;00m rubixdata.gas.coords \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[32m0\u001b[39m\n", + " \u001b[31m[... skipping hidden 2 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:621\u001b[39m, in \u001b[36mget_rubix_data\u001b[39m\u001b[34m(config)\u001b[39m\n\u001b[32m 608\u001b[39m \u001b[38;5;129m@jaxtyped\u001b[39m(typechecker=typechecker)\n\u001b[32m 609\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mget_rubix_data\u001b[39m(config: Union[\u001b[38;5;28mdict\u001b[39m, \u001b[38;5;28mstr\u001b[39m]) -> RubixData:\n\u001b[32m 610\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 611\u001b[39m \u001b[33;03m Returns the Rubix data\u001b[39;00m\n\u001b[32m 612\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 619\u001b[39m \u001b[33;03m The RubixData object containing the galaxy, stars, and gas data.\u001b[39;00m\n\u001b[32m 620\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m621\u001b[39m \u001b[43mconvert_to_rubix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m prepare_input(config)\n", + " \u001b[31m[... skipping hidden 2 frame]\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:436\u001b[39m, in \u001b[36mconvert_to_rubix\u001b[39m\u001b[34m(config)\u001b[39m\n\u001b[32m 434\u001b[39m api.load_galaxy(**config[\u001b[33m\"\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mload_galaxy_args\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 435\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m436\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnknown data source: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig[\u001b[33m'\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 438\u001b[39m \u001b[38;5;66;03m# Load the saved data into the input handler\u001b[39;00m\n\u001b[32m 439\u001b[39m logger.info(\u001b[33m\"\u001b[39m\u001b[33mLoading data into input handler\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mValueError\u001b[39m: Unknown data source: NihaoHandler." ] } ], @@ -369,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -470,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -491,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -512,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -561,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { From bb5adbd26bf031a6bd22ae07cbf089e74612cb5a Mon Sep 17 00:00:00 2001 From: anschaible Date: Sun, 25 May 2025 20:02:44 +0200 Subject: [PATCH 24/76] to calculate mock NIHAOS with mastar --- ...x_pipeline_single_function_shard_map.ipynb | 274 +++++++++++------- rubix/config/pynbody_config.yml | 8 +- rubix/core/data.py | 4 +- rubix/galaxy/input_handler/pynbody.py | 36 +++ 4 files changed, 217 insertions(+), 105 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 5767206c..e6fb4c78 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -120,16 +120,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-05-20 21:33:34,128 - rubix - INFO - \n", + "2025-05-20 22:00:18,377 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-05-20 21:33:34,129 - rubix - INFO - Rubix version: 0.0.post429+g6020fef\n", - "2025-05-20 21:33:34,129 - rubix - INFO - JAX version: 0.6.0\n", - "2025-05-20 21:33:34,129 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" + "2025-05-20 22:00:18,378 - rubix - INFO - Rubix version: 0.0.post430+g5b4f76d.d20250520\n", + "2025-05-20 22:00:18,379 - rubix - INFO - JAX version: 0.6.0\n", + "2025-05-20 22:00:18,379 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" ] } ], @@ -152,12 +152,12 @@ " \"data\": {\n", " \"name\": \"NihaoHandler\",\n", " \"args\": {\n", - " \"particle_type\": [\"stars\", \"gas\"],\n", + " \"particle_type\": [\"stars\"],\n", " \"save_data_path\": \"data\",\n", " \"snapshot\": \"1024\",\n", " },\n", " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": True, \"subset_size\": 100},\n", + " \"subset\": {\"use_subset\": False, \"subset_size\": 200000},\n", " },\n", " \"simulation\": {\n", " \"name\": \"NIHAO\",\n", @@ -228,7 +228,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 20000,\n", + " \"subset_size\": 200000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -354,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -368,7 +368,7 @@ ], "source": [ "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config_TNG)" + "pipe = RubixPipeline(config_NIHAO)" ] }, { @@ -380,23 +380,93 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-05-20 21:33:35,267 - rubix - INFO - Getting rubix data...\n" + "2025-05-20 22:00:19,477 - rubix - INFO - Getting rubix data...\n", + "2025-05-20 22:00:19,478 - rubix - INFO - Loading data into input handler\n", + "2025-05-20 22:00:19,478 - rubix - INFO - Using PynbodyHandler to load a NIHAO galaxy\n", + "2025-05-20 22:00:19,481 - rubix - INFO - Galaxy redshift (dist_z) set to: 0.1\n", + "2025-05-20 22:00:19,494 - rubix - INFO - Simulation snapshot loaded from halo 0\n", + "2025-05-20 22:00:19,572 - rubix - INFO - Halo data loaded.\n", + "2025-05-20 22:00:22,039 - rubix - WARNING - Field 'sfr' -> 'sfr' not found for gas. Assigning zeros.\n", + "2025-05-20 22:00:22,040 - rubix - WARNING - Field 'internal_energy' -> 'u' not found for gas. Assigning zeros.\n", + "2025-05-20 22:00:22,041 - rubix - WARNING - Field 'electron_abundance' -> 'electron_abundance' not found for gas. Assigning zeros.\n", + "2025-05-20 22:00:22,066 - rubix - INFO - Metals assigned to gas particles.\n", + "2025-05-20 22:00:22,066 - rubix - INFO - Metals shape is: (155341, 10)\n", + "2025-05-20 22:00:22,067 - rubix - INFO - Simulation snapshot and halo data loaded successfully for classes: ['stars', 'gas'].\n", + "2025-05-20 22:00:22,067 - rubix - DEBUG - Converting to Rubix format..\n", + "2025-05-20 22:00:22,132 - rubix - INFO - Half-mass radius calculated: 1.45 kpc\n", + "2025-05-20 22:00:22,133 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-05-20 22:00:22,134 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", + "2025-05-20 22:00:22,136 - rubix - DEBUG - Converting redshift for galaxy data into \n", + "2025-05-20 22:00:22,137 - rubix - DEBUG - Converting center for galaxy data into kpc\n", + "2025-05-20 22:00:22,138 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", + "2025-05-20 22:00:22,138 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", + "2025-05-20 22:00:22,141 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", + "2025-05-20 22:00:22,143 - rubix - DEBUG - Converting metallicity for particle type stars into \n", + "2025-05-20 22:00:22,146 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", + "2025-05-20 22:00:22,153 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", + "2025-05-20 22:00:22,160 - rubix - DEBUG - Converting density for particle type gas into Msun/kpc^3\n", + "2025-05-20 22:00:22,162 - rubix - DEBUG - Converting temperature for particle type gas into K\n", + "2025-05-20 22:00:22,163 - rubix - DEBUG - Converting metals for particle type gas into \n", + "2025-05-20 22:00:22,165 - rubix - DEBUG - Converting metallicity for particle type gas into \n", + "2025-05-20 22:00:22,166 - rubix - DEBUG - Converting coords for particle type gas into kpc\n", + "2025-05-20 22:00:22,167 - rubix - DEBUG - Converting velocity for particle type gas into km/s\n", + "2025-05-20 22:00:22,168 - rubix - DEBUG - Converting mass for particle type gas into Msun\n", + "2025-05-20 22:00:22,169 - rubix - DEBUG - Converting sfr for particle type gas into Msun/yr\n", + "2025-05-20 22:00:22,170 - rubix - DEBUG - Converting internal_energy for particle type gas into erg/g\n", + "2025-05-20 22:00:22,171 - rubix - DEBUG - Converting electron_abundance for particle type gas into \n", + "2025-05-20 22:00:22,172 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-05-20 22:00:22,222 - rubix - INFO - Centering stars particles\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted to Rubix format!\n" ] }, { - "ename": "ValueError", - "evalue": "Unknown data source: NihaoHandler.", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m#NBVAL_SKIP\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m inputdata = \u001b[43mpipe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprepare_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m rubixdata = pipe.run_sharded(inputdata)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/pipeline.py:76\u001b[39m, in \u001b[36mRubixPipeline.prepare_data\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 68\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 69\u001b[39m \u001b[33;03mPrepares and loads the data for the pipeline.\u001b[39;00m\n\u001b[32m 70\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 73\u001b[39m \u001b[33;03m 'coords', 'velocities', 'mass', 'age', and 'metallicity' under stars and gas.\u001b[39;00m\n\u001b[32m 74\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 75\u001b[39m \u001b[38;5;28mself\u001b[39m.logger.info(\u001b[33m\"\u001b[39m\u001b[33mGetting rubix data...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m76\u001b[39m rubixdata = \u001b[43mget_rubix_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43muser_config\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 77\u001b[39m star_count = (\n\u001b[32m 78\u001b[39m \u001b[38;5;28mlen\u001b[39m(rubixdata.stars.coords) \u001b[38;5;28;01mif\u001b[39;00m rubixdata.stars.coords \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[32m0\u001b[39m\n\u001b[32m 79\u001b[39m )\n\u001b[32m 80\u001b[39m gas_count = \u001b[38;5;28mlen\u001b[39m(rubixdata.gas.coords) \u001b[38;5;28;01mif\u001b[39;00m rubixdata.gas.coords \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[32m0\u001b[39m\n", - " \u001b[31m[... skipping hidden 2 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:621\u001b[39m, in \u001b[36mget_rubix_data\u001b[39m\u001b[34m(config)\u001b[39m\n\u001b[32m 608\u001b[39m \u001b[38;5;129m@jaxtyped\u001b[39m(typechecker=typechecker)\n\u001b[32m 609\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mget_rubix_data\u001b[39m(config: Union[\u001b[38;5;28mdict\u001b[39m, \u001b[38;5;28mstr\u001b[39m]) -> RubixData:\n\u001b[32m 610\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 611\u001b[39m \u001b[33;03m Returns the Rubix data\u001b[39;00m\n\u001b[32m 612\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 619\u001b[39m \u001b[33;03m The RubixData object containing the galaxy, stars, and gas data.\u001b[39;00m\n\u001b[32m 620\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m621\u001b[39m \u001b[43mconvert_to_rubix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m prepare_input(config)\n", - " \u001b[31m[... skipping hidden 2 frame]\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/rubix/lib/python3.12/site-packages/rubix/core/data.py:436\u001b[39m, in \u001b[36mconvert_to_rubix\u001b[39m\u001b[34m(config)\u001b[39m\n\u001b[32m 434\u001b[39m api.load_galaxy(**config[\u001b[33m\"\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mload_galaxy_args\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 435\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m436\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnknown data source: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig[\u001b[33m'\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 438\u001b[39m \u001b[38;5;66;03m# Load the saved data into the input handler\u001b[39;00m\n\u001b[32m 439\u001b[39m logger.info(\u001b[33m\"\u001b[39m\u001b[33mLoading data into input handler\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mValueError\u001b[39m: Unknown data source: NihaoHandler." + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-20 22:00:22,552 - rubix - INFO - Data loaded with 1043618 star particles and 0 gas particles.\n", + "2025-05-20 22:00:22,553 - rubix - INFO - Setting up the pipeline...\n", + "2025-05-20 22:00:22,553 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-05-20 22:00:22,553 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-05-20 22:00:22,555 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 22:00:23,326 - rubix - INFO - Getting cosmology...\n", + "2025-05-20 22:00:23,463 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-05-20 22:00:23,471 - rubix - INFO - Getting cosmology...\n", + "2025-05-20 22:00:23,920 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 22:00:24,399 - rubix - DEBUG - SSP Wave: (5333,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 22:00:24,410 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-20 22:00:24,545 - rubix - INFO - Assembling the pipeline...\n", + "2025-05-20 22:00:24,545 - rubix - INFO - Compiling the expressions...\n", + "2025-05-20 22:00:24,546 - rubix - INFO - Number of devices: 1\n", + "2025-05-20 22:00:24,628 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-05-20 22:00:24,730 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-05-20 22:00:24,734 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-05-20 22:00:24,760 - rubix - INFO - Calculating IFU cube...\n", + "2025-05-20 22:00:24,760 - rubix - DEBUG - Input shapes: Metallicity: 1043618, Age: 1043618\n", + "2025-05-20 22:00:24,980 - rubix - DEBUG - Calculation Finished! Spectra shape: (1043618, 5333)\n", + "2025-05-20 22:00:24,981 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-05-20 22:00:24,985 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-05-20 22:00:24,986 - rubix - DEBUG - Doppler Shifted SSP Wave: (1043618, 5333)\n", + "2025-05-20 22:00:24,986 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-05-20 22:00:25,053 - rubix - INFO - Calculating Data Cube...\n", + "2025-05-20 22:00:25,054 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-05-20 22:00:25,055 - rubix - INFO - Convolving with PSF...\n", + "2025-05-20 22:00:25,058 - rubix - INFO - Convolving with LSF...\n", + "2025-05-20 22:00:25,063 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-05-20 22:00:27,011 - rubix - INFO - Pipeline run completed in 4.46 seconds.\n" ] } ], @@ -409,98 +479,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[[ 1.8663327e-01 2.0253532e-01 2.0300621e-01 ... 2.4535783e-02\n", - " 2.8690096e-02 3.0187748e-02]\n", - " [ 2.6096473e+00 3.0808454e+00 2.9036274e+00 ... -1.0372877e+00\n", - " -8.2148838e-01 -6.4967668e-01]\n", - " [ 2.9600172e+00 4.7419906e+00 3.3595605e+00 ... -5.6377354e+00\n", - " -4.6541839e+00 -3.9757755e+00]\n", + "[[[-3.1244421e+03 -2.3109651e+03 -2.0055480e+03 ... -1.2120083e+03\n", + " -1.3375792e+03 -7.9277850e+02]\n", + " [-3.8948296e+03 -9.9477026e+02 9.6987311e+02 ... -4.2732915e+03\n", + " -4.2401475e+03 -3.9022600e+03]\n", + " [-3.6310061e+03 -1.2855078e+03 -1.1210460e+03 ... -1.1222546e+03\n", + " -1.0423126e+03 -9.7859808e+02]\n", " ...\n", - " [ 8.0857529e+01 8.7022888e+01 8.3423561e+01 ... 2.3552149e+01\n", - " 2.2909094e+01 2.1220167e+01]\n", - " [ 2.0778951e+02 2.2062862e+02 2.1599808e+02 ... 6.6492391e+00\n", - " 6.4269590e+00 5.9624753e+00]\n", - " [ 5.6667694e+01 6.0071331e+01 5.9009029e+01 ... 9.3002629e-01\n", - " 9.1504198e-01 8.6860436e-01]]\n", + " [-2.7880623e+03 -1.9734590e+03 -5.0688306e+02 ... -3.1797351e+03\n", + " -3.4406570e+03 -3.4141396e+03]\n", + " [-1.9713757e+03 -1.9807825e+03 -9.0053284e+02 ... -4.1801104e+03\n", + " -4.3672441e+03 -4.1323481e+03]\n", + " [ 4.9658901e+01 -5.7049971e+03 -6.4394551e+03 ... -2.7122344e+03\n", + " -2.4240444e+03 -2.2186223e+03]]\n", "\n", - " [[ 8.8623657e+00 9.4128408e+00 9.5636234e+00 ... 2.6624138e+00\n", - " 2.7262561e+00 2.6511188e+00]\n", - " [ 3.9778210e+01 4.2250408e+01 4.2992622e+01 ... 1.0783029e+01\n", - " 1.1080705e+01 1.0803394e+01]\n", - " [-4.1083000e+01 -4.1158161e+01 -4.3862789e+01 ... -9.1105967e+00\n", - " -8.0653925e+00 -7.5847492e+00]\n", + " [[-6.1910991e+03 -6.1451768e+03 -6.2967378e+03 ... -2.3543020e+03\n", + " -2.4165703e+03 -2.4096201e+03]\n", + " [-5.3934741e+03 -5.0396533e+03 -4.5310410e+03 ... -4.8921401e+03\n", + " -4.7913770e+03 -4.7794272e+03]\n", + " [-7.0857314e+03 -6.5290640e+03 -6.1235781e+03 ... -5.0580137e+03\n", + " -5.0012476e+03 -4.9760371e+03]\n", " ...\n", - " [ 3.6464722e+02 3.8163013e+02 3.7437909e+02 ... 4.9800919e+01\n", - " 4.9048306e+01 4.6190613e+01]\n", - " [ 9.7805939e+02 1.0347834e+03 1.0180806e+03 ... 4.0949604e+01\n", - " 3.9178234e+01 3.5780865e+01]\n", - " [ 5.5939709e+02 5.9595532e+02 5.9040039e+02 ... 2.0157406e+01\n", - " 2.0081875e+01 1.8848007e+01]]\n", + " [-6.4192046e+03 -5.3413877e+03 -5.0410649e+03 ... -4.3669092e+03\n", + " -4.9825347e+03 -5.0488794e+03]\n", + " [-5.7569917e+03 -5.2280347e+03 -4.5223115e+03 ... -5.4029375e+03\n", + " -5.5996426e+03 -5.7261963e+03]\n", + " [-3.8117400e+03 -5.0391382e+03 -3.8952700e+03 ... -3.9984387e+03\n", + " -3.6006785e+03 -3.1596533e+03]]\n", "\n", - " [[-3.1875231e+01 -3.2292850e+01 -3.5248451e+01 ... 3.8041861e+00\n", - " 4.3648586e+00 4.3608699e+00]\n", - " [ 1.4530573e+02 1.5422511e+02 1.5667447e+02 ... 4.3676464e+01\n", - " 4.4651451e+01 4.3363514e+01]\n", 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1.0970240e+03 4.8213348e+02 ... -1.4007925e+03\n", + " -1.2085845e+03 -1.1107657e+03]\n", + " [-1.5701598e+03 -1.0600278e+03 -5.9062360e+02 ... -7.4803308e+02\n", + " -5.0066211e+02 -6.3199823e+02]]]\n" ] } ], @@ -510,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -531,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -552,12 +622,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -601,12 +671,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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noPe9yc6dOyWV/zyqX79+uYkrfvjhB3333Xflxj8sU/Y5XHYf9OdklSR16tSpwv180a1bNyUlJVmKYdf9yJ/vEX29DhX59ttvdd9992nlypXlvnAsi+Gv+xFJ6tixoxYtWiSpdKgBY4zuv/9+3X///RXG2Lt3r0fSCPDFqlWr9MQTT2jt2rX6+eeftXTpUo0YMaLK+z/wwAOaPn16ufUNGjRwd1/1NxI+QABERUUpLi5O33zzzTG3++abb9SqVStFRkZ6rPfX7FCVzdz15xuIRx55RPfff7+uvfZaPfTQQ+4+7JMmTfJ71YfT6dSRI0cq/FlZEsW7MsZXP/74o3Jzcz0SZ1dddZVWr16tv/71r+rZs6caNWokl8ul888/v0rnYNu2bTr33HPVuXNnzZgxQwkJCQoLC9O7776rp59+2ufzWNlNZElJSbVmYys7/hNPPKGePXtWuE2jRo18jgsACKywsDD16dNHffr0UceOHTVhwgQtXrxY06ZNc29TlXuB2267TfPmzdOkSZPUr18/RUVFyeFw6Oqrr67wM8zKfYzL5VK3bt00Y8aMCn/+5/F+AiU8PLzG70eOHj2q77//3iPp4ut18HbgwAENGjRIkZGRevDBB9WhQweFh4frq6++0j333FOt+xFTwViTFQ0AXRVlx7/rrruUnJxc4Ta+fLEJeMvPz1ePHj107bXX6rLLLvN5/7vuuqtcT4xzzz1Xffr0sauJPiPhAwTIRRddpLlz5+qTTz5xz7T1Z//73/+0Y8cO3XTTTdWK36ZNG7lcLmVlZXl8Y7J169Zqt7kiS5Ys0ZAhQ/TSSy95rD9w4IDfB1Ru06aNtmzZUuHPyta3adPG0jH+9a9/SZL7RuO3335Tenq6pk+frqlTp7q3K6uK+bPKEjFvvfWWCgsLtXz5co9vUsvK0suUfUu5cePGY97QNG3aVAcOHCi3fufOneW+Ka2orcYYbd26Vd27d/c4bmRkpOVvNAEAtVNZBe3PP//s875LlizRuHHj9NRTT7nXFRQUVPhZVJGyz+YffvjB3TVLKk1qZGVlqUePHu51HTp00Pr163Xuueces0qm7D5o27ZtHlU9ld0n2KlNmzbatGlThT+z635kyZIlOnLkiEfio6rXobLzlpGRof379+vNN9/U2Wef7V6flZXlsd2f70eOdV/QtGlTj65/ZcoqurxVdO/0/fffu2f0KruHqV+/PvcjqBHDhg3TsGHDKv15YWGh/va3v+mNN97QgQMH1LVrVz322GPu2egaNWrk8SXo+vXrtWnTJs2ZM6emm14pxvABAuSvf/2rIiIidNNNN3lM3y2VdhG6+eab1aBBA/dU3r4quwF44YUXPNY/99xz1WtwJUJDQ8t9e7N48eKA9KG+4IILtGbNGq1du9Zj/YEDB/Tvf/9bPXv2tNTHfuXKlXrooYfUrl07jR49WtIf33x6n4OZM2eW279hw4bu9vxZRTFyc3M1b948j+2GDh2qxo0bKy0tTQUFBR4/+/O+HTp00Jo1a1RUVORe9/bbb1c41br0x6wYZZYsWaKff/7Z/YHXq1cvdejQQU8++aQOHTpUbv9ffvmlwrgAgNrno48+qrDqomyclOp0earoXuC5556rciVH79691bJlS82ZM8fjs2v+/PnlPjOvuuoq7dmzR3Pnzi0X58iRI+5uE2WfYc8++6zHNhV9Ptvtggsu0I8//ugxY5hU+sfiP//5T0VHR+uMM86odvz169dr0qRJatq0qcdMnlW9Dr7cjxQVFZW7lzzjjDPUrl0799Twf+Z9P7J582aP+4T169fr008/rfB5LVu2zOP+8fPPP9dnn33mvpbR0dEaPHiw/vGPf1SYmOR+BDVt4sSJyszM1IIFC/TNN9/oyiuv1Pnnn19hslKS/vnPf6pjx46VzsrsD1T4AAFyyimn6JVXXtHo0aPVrVs3XXfddWrXrp127Nihl156Sfv27dMbb7xRru95VfXq1UuXX365Zs6cqf3797unZf/+++8l2dN3XCqtVHrwwQc1YcIE9e/fXxs2bNC///3vCitJatrkyZO1ePFinX322brpppvUuXNn/fTTT5o/f75+/vnncgmUY3nvvfe0efNmFRcXKycnRytXrtQHH3ygNm3aaPny5e5S7MjISJ199tl6/PHHdfToUbVq1Ur//e9/y30bJpVeE0n629/+pquvvlr169fX8OHDNXToUIWFhWn48OG66aabdOjQIc2dO1fR0dEeNzSRkZF6+umndf3116tPnz665ppr1LRpU61fv16HDx/WK6+8Iql0GtolS5bo/PPP11VXXaVt27bptddeq/S11KxZM5111lmaMGGCcnJyNHPmTJ188snuaedDQkL0z3/+U8OGDVOXLl00YcIEtWrVSnv27NFHH32kyMhIvfXWW1U+twCAwLntttt0+PBhXXrppercubOKioq0evVqLVy4UG3bttWECRN8jnnRRRfpX//6l6KionTaaacpMzNTH374YZXH8qtfv77+/ve/66abbtI555yjkSNHKisrS/PmzSt3PzFmzBgtWrRIN998sz766CMNGDBAJSUl2rx5sxYtWqT3339fvXv3Vs+ePTVq1Ci98MILys3NVf/+/ZWenm57pXNFbrzxRr388su68sorde211+r000/X/v37tXDhQm3cuFGvvvqqx+DUx/K///1PBQUF7okxPv30Uy1fvlxRUVFaunSpxxdZVb0OPXv2VGhoqB577DHl5ubK6XTqnHPOUf/+/dW0aVONGzdOt99+uxwOh/71r3+VSyKFhIRo9uzZGj58uHr27KkJEyYoLi5Omzdv1rfffqv3339fknTttddqxowZSk5O1nXXXae9e/dqzpw56tKlS7nxgaTS7lhnnXWWbrnlFhUWFmrmzJlq3ry57r77bvc2s2bN0llnnaVu3brphhtuUPv27ZWTk6PMzEz9+OOPWr9+fZWvE+CLXbt2ad68edq1a5fi4+MllXbhWrFihebNm6dHHnnEY/uCggL9+9//1uTJkwPR3D/4fV4wAB6++eYbM2rUKBMXF2fq169vYmNjzahRo8yGDRvKbVs29fovv/xS6c/+LD8/36SkpJhmzZqZRo0amREjRpgtW7YYSebRRx91b1fZtOwVTSnqPcVmQUGB+ctf/mLi4uJMRESEGTBggMnMzCy3nR3Tsh9rqvQyP/74o7n++utNq1atTL169UyzZs3MRRddZNasWXPcff98rLIlLCzMxMbGmvPOO88888wzHtOX//mYl156qWnSpImJiooyV155pfnpp58qnPb1oYceMq1atTIhISEe53z58uWme/fuJjw83LRt29Y89thj5uWXX65wGvfly5eb/v37m4iICBMZGWn69u1r3njjDY9tnnrqKdOqVSvjdDrNgAEDzJdfflnptOxvvPGGmTJliomOjjYRERHmwgsvNDt37iz3PL/++mtz2WWXmebNmxun02natGljrrrqKpOenl6lcwsACLz33nvPXHvttaZz586mUaNGJiwszJx88snmtttuMzk5OR7bSjIpKSnlYrRp08aMGzfO/fi3334zEyZMMC1atDCNGjUyycnJZvPmzeW2O97n+QsvvGDatWtnnE6n6d27t1m1alWFU3sXFRWZxx57zHTp0sU4nU7TtGlT06tXLzN9+nSTm5vr3u7IkSPm9ttvN82bNzcNGzY0w4cPN7t377Y8Lfuxpkr/8zm58847Tbt27Uz9+vVNZGSkGTJkiHnvvfeOu++fj1W21K9f37Rs2dKcffbZ5uGHHzZ79+6t8JhVuQ7GGDN37lzTvn17Exoa6jFF+6effmrOPPNMExERYeLj483dd99t3n///Qqncf/kk0/MeeedZxo3bmwaNmxounfvbp577jmPbV577TXTvn17ExYWZnr27Gnef//9Sqdlf+KJJ8xTTz1lEhISjNPpNAMHDjTr168v9zy3bdtmxo4da2JjY039+vVNq1atzEUXXWSWLFlSpXMLVIUks3TpUvfjt99+20gyDRs29Fjq1atnrrrqqnL7v/7666ZevXomOzvbj60uz2FMBTWdAILWunXrdPrpp+u1115zd0sCAAAAAJRyOBwes3QtXLhQo0eP1rfffltuMPtGjRqVGzbi3HPPVWRkpJYuXeqvJleILl1AEDty5Ei5mTBmzpypkJAQj8H4AAAAAAAVO/3001VSUqK9e/ced0yerKwsffTRR1q+fLmfWlc5Ej5AEHv88ce1du1aDRkyRPXq1dN7772n9957TzfeeGOtmLYUAAAAAGqDQ4cOeYzzlZWVpXXr1qlZs2bq2LGjRo8erbFjx+qpp57S6aefrl9++UXp6enq3r27LrzwQvd+L7/8suLi4o4545e/0KULCGIffPCBpk+frk2bNunQoUNq3bq1xowZo7/97W+qV498LwAAAABIUkZGhoYMGVJu/bhx4zR//nwdPXpUf//73/Xqq69qz549atGihc4880xNnz5d3bp1kyS5XC61adNGY8eO1cMPP+zvp1COzwmfVatW6YknntDatWv1888/e/Rrq0xGRoZSU1P17bffKiEhQffdd5/Gjx9vodkAAAAAAACoTIivO+Tn56tHjx6aNWtWlbbPysrShRdeqCFDhmjdunWaNGmSrr/+evd0fQAAAAAAALCXpS5d3iNXV+See+7RO++8o40bN7rXXX311Tpw4IBWrFhR3UMDAAAAAACgEjU+iEdmZqaSkpI81iUnJ2vSpEmV7lNYWKjCwkL3Y5fLpV9//VXNmzeXw+GoqaYCAADIGKODBw8qPj5eISE+F0PXeS6XSz/99JMaN27MfRcAoNbw9+dzQUGBioqKbI0ZFham8PBwW2MeS40nfLKzsxUTE+OxLiYmRnl5eRVOGS1JaWlpmj59ek03DQAAoFK7d+/WSSedFOhm+N1PP/3ETI4AgFrLH5/PBQUFatcuVtnZubbGjY2NVVZWlt+SPrVymp4pU6YoNTXV/Tg3N1etW7f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", 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"text/plain": [ "
" ] diff --git a/rubix/config/pynbody_config.yml b/rubix/config/pynbody_config.yml index 2d9ff362..d25f0459 100644 --- a/rubix/config/pynbody_config.yml +++ b/rubix/config/pynbody_config.yml @@ -9,7 +9,10 @@ fields: gas: density: "rho" temperature: "temp" - metallicity: "metals" + metals: "metals" + #OxMassFrac: "OxMassFrac" + #HI: "HI" + metallicity: metals coords: "pos" velocity: "vel" mass: "mass" @@ -28,6 +31,9 @@ units: gas: density: "Msun/kpc^3" temperature: "K" + metals: "dimensionless" + #OxMassFrac: "dimensionless" + #HI: "dimensionless" metallicity: "Zsun" coords: "kpc" velocity: "km/s" diff --git a/rubix/core/data.py b/rubix/core/data.py index d00478f0..d22b87b2 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -432,8 +432,8 @@ def convert_to_rubix(config: Union[dict, str]): logger.info("Loading data from IllustrisAPI") api = IllustrisAPI(**config["data"]["args"], logger=logger) api.load_galaxy(**config["data"]["load_galaxy_args"]) - else: - raise ValueError(f"Unknown data source: {config['data']['name']}.") + #else: + # raise ValueError(f"Unknown data source: {config['data']['name']}.") # Load the saved data into the input handler logger.info("Loading data into input handler") diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index fe2beec5..c5ce8118 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -84,6 +84,42 @@ def load_data(self): getattr(self.sim, cls), fields[cls], units[cls], cls ) + # for cls in self.data: + # self.logger.info(f"Loaded {cls} data: {self.data[cls].keys()}") + # self.logger.info("Assigning metals to gas particles........") + + # Combine HI and OxMassFrac into a two-column metals field for gas + # self.data["gas"]["metals"] = np.column_stack((self.data["gas"]["HI"], + # self.data["gas"]["OxMassFrac"])) + # self.logger.info("Metals assigned to gas particles........") + # self.logger.info("Metals shape is: ", self.data["gas"]["metals"].shape) + + hi_data = self.load_particle_data( + getattr(self.sim, "gas"), + {"HI": "HI"}, + {"HI": u.dimensionless_unscaled}, + "gas", + ) + ox_data = self.load_particle_data( + getattr(self.sim, "gas"), + {"OxMassFrac": "OxMassFrac"}, + {"OxMassFrac": u.dimensionless_unscaled}, + "gas", + ) + # fe_data = self.load_particle_data(getattr(self.sim, "gas"), {"FeMassFrac": "FeMassFrac"}, {"FeMassFrac": u.dimensionless_unscaled}, "gas") + # self.data["gas"]["metals"] = np.column_stack((hi_data["HI"], ox_data["OxMassFrac"])) + # Create a metals array with 10 columns, filled with zeros initially + n_particles = hi_data["HI"].shape[0] + metals = np.zeros((n_particles, 10), dtype=hi_data["HI"].dtype) + + # Place HI values at column 0 and OxMassFrac (O) at column 4 (that it is storred in the same way as IllustrisTNG) + metals[:, 0] = hi_data["HI"] + metals[:, 4] = ox_data["OxMassFrac"] + + self.data["gas"]["metals"] = metals + self.logger.info("Metals assigned to gas particles.") + self.logger.info("Metals shape is: %s", self.data["gas"]["metals"].shape) + self.logger.info( f"Simulation snapshot and halo data loaded successfully for classes: {load_classes}." ) From e107c134f90c88184b385fffc0e0a951ffb0ae2a Mon Sep 17 00:00:00 2001 From: anschaible Date: Mon, 26 May 2025 13:01:56 +0200 Subject: [PATCH 25/76] issue with memory and underflow/overflow --- ...eline_single_function_shard_map_fits.ipynb | 693 ++++++++++++++++++ rubix/core/fits.py | 5 +- rubix/spectra/ifu.py | 2 +- rubix/telescope/telescopes.yaml | 10 + 4 files changed, 708 insertions(+), 2 deletions(-) create mode 100644 notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb new file mode 100644 index 00000000..a9a91ca8 --- /dev/null +++ b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb @@ -0,0 +1,693 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#from jax import config\n", + "#config.update(\"jax_enable_x64\", True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0)]\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import os\n", + "\n", + "# Tell XLA to fake 2 host CPU devices\n", + "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", + "\n", + "# Only make GPU 0 and GPU 1 visible to JAX:\n", + "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", + "\n", + "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", + "\n", + "import jax\n", + "\n", + "# Now JAX will list two CpuDevice entries\n", + "print(jax.devices())\n", + "# → [CpuDevice(id=0), CpuDevice(id=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "#import os\n", + "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", + "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RUBIX pipeline\n", + "\n", + "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", + "\n", + "## How to use the Pipeline\n", + "1) Define a `config`\n", + "2) Setup the `pipeline yaml`\n", + "3) Run the RUBIX pipeline\n", + "4) Do science with the mock-data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Config\n", + "\n", + "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", + "\n", + "For the `config` you can choose the following options:\n", + "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", + "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", + "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", + "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", + "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", + "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", + "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", + "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", + "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", + "- `simulation - name`: currently only IllustrisTNG is supported\n", + "- `simulation - args - path`: where the data is stored and how the file will be named\n", + "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", + "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", + "- `telescope - psf`: define the point spread function that is applied to the mock data\n", + "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", + "- `telescope - noise`: define the noise that is applied to the mock data\n", + "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", + "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", + "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", + "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-26 11:52:39,915 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-05-26 11:52:39,916 - rubix - INFO - Rubix version: 0.0.post431+gbb5adbd.d20250526\n", + "2025-05-26 11:52:39,916 - rubix - INFO - JAX version: 0.6.0\n", + "2025-05-26 11:52:39,917 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "\n", + "galaxy_id = \"g7.66e11\"\n", + "\n", + "config_NIHAO = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"NihaoHandler\",\n", + " \"args\": {\n", + " \"particle_type\": [\"stars\"],\n", + " \"save_data_path\": \"data\",\n", + " \"snapshot\": \"1024\",\n", + " },\n", + " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", + " \"subset\": {\"use_subset\": False, \"subset_size\": 200000},\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"NIHAO\",\n", + " \"args\": {\n", + " \"path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024',\n", + " \"halo_path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024.z0.000.AHF_halos',\n", + " \"halo_id\": 0,\n", + " },\n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE_WFM\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.01,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"BruzualCharlot2003\" #\"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "config_TNG = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 12,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": False,\n", + " \"subset_size\": 200000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-12.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE_WFM\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.01,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Pipeline yaml\n", + "\n", + "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", + "\n", + "```yaml\n", + "calc_ifu:\n", + " Transformers:\n", + " rotate_galaxy:\n", + " name: rotate_galaxy\n", + " depends_on: null\n", + " args: []\n", + " kwargs:\n", + " type: \"face-on\"\n", + " filter_particles:\n", + " name: filter_particles\n", + " depends_on: rotate_galaxy\n", + " args: []\n", + " kwargs: {}\n", + " spaxel_assignment:\n", + " name: spaxel_assignment\n", + " depends_on: filter_particles\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " reshape_data:\n", + " name: reshape_data\n", + " depends_on: spaxel_assignment\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " calculate_spectra:\n", + " name: calculate_spectra\n", + " depends_on: reshape_data\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " scale_spectrum_by_mass:\n", + " name: scale_spectrum_by_mass\n", + " depends_on: calculate_spectra\n", + " args: []\n", + " kwargs: {}\n", + " doppler_shift_and_resampling:\n", + " name: doppler_shift_and_resampling\n", + " depends_on: scale_spectrum_by_mass\n", + " args: []\n", + " kwargs: {}\n", + " calculate_datacube:\n", + " name: calculate_datacube\n", + " depends_on: doppler_shift_and_resampling\n", + " args: []\n", + " kwargs: {}\n", + " convolve_psf:\n", + " name: convolve_psf\n", + " depends_on: calculate_datacube\n", + " args: []\n", + " kwargs: {}\n", + " convolve_lsf:\n", + " name: convolve_lsf\n", + " depends_on: convolve_psf\n", + " args: []\n", + " kwargs: {}\n", + " apply_noise:\n", + " name: apply_noise\n", + " depends_on: convolve_lsf\n", + " args: []\n", + " kwargs: {}\n", + "```\n", + "\n", + "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the pipeline\n", + "\n", + "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config_TNG)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-26 11:52:41,325 - rubix - INFO - Getting rubix data...\n", + "2025-05-26 11:52:41,326 - rubix - INFO - Loading data from IllustrisAPI\n", + "2025-05-26 11:52:41,327 - rubix - DEBUG - Loading galaxy with ID 12\n", + "2025-05-26 11:52:41,327 - rubix - DEBUG - Performing GET request from http://www.tng-project.org/api/TNG50-1/snapshots/99/subhalos/12/cutout.hdf5?stars=Coordinates,GFM_InitialMass,GFM_Metallicity,GFM_StellarFormationTime,Velocities, with parameters None\n", + "2025-05-26 11:52:43,269 - rubix - DEBUG - Performing GET request from http://www.tng-project.org/api/TNG50-1/snapshots/99/subhalos/12, with parameters None\n", + "2025-05-26 11:52:43,456 - rubix - DEBUG - Appending subhalo data for subhalo 12\n", + "2025-05-26 11:52:43,500 - rubix - INFO - Loading data into input handler\n", + "2025-05-26 11:52:43,500 - rubix - DEBUG - Loading data from Illustris file..\n", + "2025-05-26 11:52:43,501 - rubix - DEBUG - Checking if the fields are present in the file...\n", + "2025-05-26 11:52:43,501 - rubix - DEBUG - Keys in the file: \n", + "2025-05-26 11:52:43,502 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", + "2025-05-26 11:52:43,502 - rubix - DEBUG - Matching fields: {'SubhaloData', 'Header', 'PartType4'}\n", + "2025-05-26 11:52:43,506 - rubix - DEBUG - Found 649384 valid particles out of 649384\n", + "2025-05-26 11:52:43,977 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", + "2025-05-26 11:52:54,030 - rubix - DEBUG - Converting to Rubix format..\n", + "2025-05-26 11:52:54,032 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", + "2025-05-26 11:52:54,032 - rubix - DEBUG - Keys in the particle data: dict_keys(['stars'])\n", + "2025-05-26 11:52:54,033 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", + "2025-05-26 11:52:54,033 - rubix - DEBUG - Matching fields: {'stars'}\n", + "2025-05-26 11:52:54,034 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", + "2025-05-26 11:52:54,034 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", + "2025-05-26 11:52:54,035 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-05-26 11:52:54,036 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", + "2025-05-26 11:52:54,040 - rubix - DEBUG - Converting redshift for galaxy data into \n", + "2025-05-26 11:52:54,041 - rubix - DEBUG - Converting center for galaxy data into kpc\n", + "2025-05-26 11:52:54,042 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", + "2025-05-26 11:52:54,043 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", + "2025-05-26 11:52:54,052 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", + "2025-05-26 11:52:54,075 - rubix - DEBUG - Converting metallicity for particle type stars into \n", + "2025-05-26 11:52:54,077 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", + "2025-05-26 11:52:54,079 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", + "2025-05-26 11:52:54,086 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-05-26 11:52:54,120 - rubix - INFO - Centering stars particles\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted to Rubix format!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-26 11:52:54,556 - rubix - INFO - Data loaded with 649384 star particles and 0 gas particles.\n", + "2025-05-26 11:52:54,558 - rubix - INFO - Setting up the pipeline...\n", + "2025-05-26 11:52:54,558 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-05-26 11:52:54,559 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-05-26 11:52:54,561 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-26 11:52:54,926 - rubix - INFO - Getting cosmology...\n", + "2025-05-26 11:52:55,125 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-05-26 11:52:55,137 - rubix - INFO - Getting cosmology...\n", + "2025-05-26 11:52:55,723 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-26 11:52:56,333 - rubix - DEBUG - SSP Wave: (5333,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-26 11:52:56,349 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-26 11:52:56,556 - rubix - INFO - Assembling the pipeline...\n", + "2025-05-26 11:52:56,557 - rubix - INFO - Compiling the expressions...\n", + "2025-05-26 11:52:56,558 - rubix - INFO - Number of devices: 1\n", + "2025-05-26 11:52:56,664 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-05-26 11:52:56,804 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-05-26 11:52:56,811 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-05-26 11:52:56,846 - rubix - INFO - Calculating IFU cube...\n", + "2025-05-26 11:52:56,847 - rubix - DEBUG - Input shapes: Metallicity: 649384, Age: 649384\n", + "2025-05-26 11:52:57,157 - rubix - DEBUG - Calculation Finished! Spectra shape: (649384, 5333)\n", + "2025-05-26 11:52:57,158 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-05-26 11:52:57,164 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-05-26 11:52:57,165 - rubix - DEBUG - Doppler Shifted SSP Wave: (649384, 5333)\n", + "2025-05-26 11:52:57,165 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-05-26 11:52:57,257 - rubix - INFO - Calculating Data Cube...\n", + "2025-05-26 11:52:57,260 - rubix - DEBUG - Datacube Shape: (300, 300, 3721)\n", + "2025-05-26 11:52:57,261 - rubix - INFO - Convolving with PSF...\n", + "2025-05-26 11:52:57,265 - rubix - INFO - Convolving with LSF...\n", + "2025-05-26 11:52:57,271 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-05-26 11:53:00,094 - rubix - INFO - Pipeline run completed in 5.54 seconds.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "inputdata = pipe.prepare_data()\n", + "rubixdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#print(rubixdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert luminosity to flux" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from rubix.spectra.ifu import convert_luminoisty_to_flux\n", + "from rubix.cosmology import PLANCK15\n", + "\n", + "observation_lum_dist = PLANCK15.luminosity_distance_to_z(config_NIHAO[\"galaxy\"][\"dist_z\"])\n", + "observation_z = config_NIHAO[\"galaxy\"][\"dist_z\"]\n", + "pixel_size = 1.0\n", + "fluxcube = convert_luminoisty_to_flux(rubixdata, observation_lum_dist, observation_z, pixel_size)\n", + "rubixdata = fluxcube/1e-20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Store datacube in a fits file with header" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-26 12:02:55,333 - rubix - INFO - Datacube saved to ./output/NIHAO_idg7.66e11_snap1024_subsetFalse.fits\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "from rubix.core.fits import store_fits\n", + "\n", + "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_ultraWFM\":\n", + "# cutted_datatcube = data.stars.datacube[300:600, :, :]\n", + "# data.stars.datacube = cutted_datatcube\n", + "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_WFM\":\n", + "# cutted_datatcube = data.stars.datacube[100:200, :, :]\n", + "# data.stars.datacube = cutted_datatcube\n", + "\n", + "store_fits(config_NIHAO, rubixdata, \"./output/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Mock-data\n", + "\n", + "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import jax.numpy as jnp\n", + "\n", + "wave = pipe.telescope.wave_seq\n", + "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", + "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", + "spectra_sharded = rubixdata # Spectra of all stars\n", + "#print(spectra.shape)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "#plt.subplot(1, 2, 1)\n", + "#plt.title(\"Rubix\")\n", + "#plt.xlabel(\"Wavelength [Angstrom]\")\n", + "#plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "#plt.plot(wave, spectra[12,12,:])\n", + "#plt.plot(wave, spectra[8,12,:])\n", + "\n", + "#plt.subplot(1, 2, 2)\n", + "plt.title(\"Rubix Sharded\")\n", + "plt.xlabel(\"Wavelength [Angstrom]\")\n", + "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "plt.plot(wave, spectra_sharded[21,15,:])\n", + "plt.plot(wave, spectra_sharded[15,21,:])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot a spacial image of the data cube" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import numpy as np\n", + "# get the spectra of the visible wavelengths from the ifu cube\n", + "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", + "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "#visible_spectra.shape\n", + "\n", + "#image = jnp.sum(visible_spectra, axis=2)\n", + "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", + "img32 = np.array(sharded_image, dtype=np.float32)\n", + "\n", + "# Plot side by side\n", + "plt.figure(figsize=(6, 5))\n", + "\n", + "# Original IFU datacube image\n", + "#im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", + "#axes[0].set_title(\"Original IFU Datacube\")\n", + "#fig.colorbar(im0, ax=axes[0])\n", + "\n", + "# Sharded IFU datacube image\n", + "plt.imshow(img32, origin=\"lower\", cmap=\"inferno\")\n", + "plt.title(\"Sharded IFU Datacube\")\n", + "plt.colorbar(label=\"Flux [erg/s/cm^2]\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DONE!\n", + "\n", + "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/rubix/core/fits.py b/rubix/core/fits.py index 9a3dceac..fe74655d 100644 --- a/rubix/core/fits.py +++ b/rubix/core/fits.py @@ -22,6 +22,7 @@ def store_fits(config, data, filepath): logger_config = config.get("logger", None) logger = get_logger(logger_config) + """ if "cube_type" not in config["data"]["args"]: datacube = data.stars.datacube parttype = "stars" @@ -31,6 +32,8 @@ def store_fits(config, data, filepath): elif config["data"]["args"]["cube_type"] == "gas": datacube = data.gas.datacube parttype = "gas" + """ + datacube = data telescope = get_telescope(config) @@ -89,7 +92,7 @@ def store_fits(config, data, filepath): output_filename = ( f"{filepath}{config['simulation']['name']}_id{galaxy_id}_snap{snapshot}_" - f"{parttype}_subset{config['data']['subset']['use_subset']}.fits" + f"subset{config['data']['subset']['use_subset']}.fits" ) os.makedirs(os.path.dirname(output_filename), exist_ok=True) diff --git a/rubix/spectra/ifu.py b/rubix/spectra/ifu.py index 191aab83..483106b2 100644 --- a/rubix/spectra/ifu.py +++ b/rubix/spectra/ifu.py @@ -31,7 +31,7 @@ def convert_luminoisty_to_flux( Returns: The flux of the object in units erg/s/cm^2/Angstrom as observed by the telescope (array-like). """ - CONST = CONSTANTS.get("LSOL_TO_ERG") / CONSTANTS.get("MPC_TO_CM") ** 2 + CONST = float(CONSTANTS.get("LSOL_TO_ERG")) / float(CONSTANTS.get("MPC_TO_CM")) ** 2 FACTOR = ( CONST / (4 * jnp.pi * observation_lum_dist**2) diff --git a/rubix/telescope/telescopes.yaml b/rubix/telescope/telescopes.yaml index d833797f..b1cd3518 100644 --- a/rubix/telescope/telescopes.yaml +++ b/rubix/telescope/telescopes.yaml @@ -9,6 +9,16 @@ MUSE: aperture_type: "square" pixel_type: "square" +MUSE_WFM: + fov: 60.0 + spatial_res: 0.2 + wave_range: [4700.15, 9351.4] + wave_res: 1.25 + lsf_fwhm: 2.51 + signal_to_noise: null + aperture_type: "square" + pixel_type: "square" + NIRSpec_PRISM_CLEAR: fov: 3.0 spatial_res: 0.1 From 0c0ae515585c64a940429be96f8eb554f93b7349 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 26 May 2025 11:19:33 +0000 Subject: [PATCH 26/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- notebooks/debug_spectra_lookup.ipynb | 256 +------ notebooks/filter_curves.ipynb | 557 +------------- notebooks/psf.ipynb | 1 - notebooks/rubix_pipeline_sharding.py | 88 +-- .../rubix_pipeline_single_function.ipynb | 151 +--- ...x_pipeline_single_function_shard_map.ipynb | 280 +------ ...eline_single_function_shard_map_fits.ipynb | 197 +---- notebooks/rubix_pipeline_stepwise.ipynb | 181 +---- notebooks/ssp_template.ipynb | 719 +----------------- rubix/__init__.py | 2 - rubix/config/config.py | 3 +- rubix/config/rubix_config.yml | 2 +- rubix/core/cosmology.py | 6 +- rubix/core/data.py | 4 +- rubix/core/dust.py | 25 +- rubix/core/fits.py | 14 +- rubix/core/ifu.py | 56 +- rubix/core/lsf.py | 11 +- rubix/core/noise.py | 14 +- rubix/core/pipeline.py | 281 +++---- rubix/core/psf.py | 15 +- rubix/core/rotation.py | 8 +- rubix/core/ssp.py | 10 +- rubix/core/telescope.py | 23 +- rubix/core/visualisation.py | 8 +- rubix/cosmology/__init__.py | 1 - rubix/cosmology/base.py | 11 +- rubix/cosmology/utils.py | 7 +- rubix/debug.py | 7 +- rubix/galaxy/__init__.py | 2 +- rubix/galaxy/alignment.py | 5 +- rubix/galaxy/input_handler/__init__.py | 5 +- .../galaxy/input_handler/api/illustris_api.py | 6 +- rubix/galaxy/input_handler/base.py | 14 +- rubix/galaxy/input_handler/factory.py | 10 +- rubix/galaxy/input_handler/illustris.py | 7 +- rubix/galaxy/input_handler/pynbody.py | 14 +- rubix/pipeline/abstract_pipeline.py | 4 +- rubix/pipeline/transformer.py | 3 +- rubix/spectra/dust/dust_baseclasses.py | 42 +- rubix/spectra/dust/extinction_models.py | 198 +++-- rubix/spectra/dust/generic_models.py | 76 +- rubix/spectra/dust/helpers.py | 50 +- rubix/spectra/ssp/factory.py | 24 +- rubix/spectra/ssp/fsps_grid.py | 15 +- rubix/spectra/ssp/grid.py | 20 +- rubix/telescope/apertures.py | 5 +- rubix/telescope/base.py | 6 +- rubix/telescope/factory.py | 14 +- rubix/telescope/filters/__init__.py | 2 +- rubix/telescope/filters/filters.py | 22 +- rubix/telescope/lsf/lsf.py | 4 +- rubix/telescope/noise/noise.py | 1 - rubix/telescope/psf/kernels.py | 2 +- rubix/telescope/psf/psf.py | 3 +- rubix/telescope/utils.py | 9 +- rubix/units.py | 2 +- rubix/utils.py | 7 +- setup.py | 1 - tests/test_apertures.py | 5 +- tests/test_core_cosmology.py | 3 +- tests/test_core_data.py | 13 +- tests/test_core_ifu.py | 12 +- tests/test_core_lsf.py | 3 +- tests/test_core_pipeline.py | 21 +- tests/test_core_psf.py | 1 + tests/test_core_rotation.py | 1 + tests/test_core_ssp.py | 9 +- tests/test_core_telescope.py | 10 +- tests/test_cosmology.py | 3 +- tests/test_debug.py | 7 +- tests/test_dust_classes.py | 51 +- tests/test_dust_extinction.py | 123 +-- tests/test_factory.py | 4 +- tests/test_galaxy_alignment.py | 15 +- tests/test_illustris_handler.py | 6 +- tests/test_input_handler.py | 5 +- tests/test_pipeline.py | 12 +- tests/test_pynbody_handler.py | 25 +- tests/test_spectra_ifu.py | 15 +- tests/test_ssp_factory.py | 19 +- tests/test_ssp_fsps.py | 16 +- tests/test_telescope_factory.py | 20 +- tests/test_telescope_filters.py | 21 +- tests/test_telescope_lsf.py | 1 + tests/test_telescope_noise.py | 3 +- tests/test_telescope_psf.py | 7 +- tests/test_telescope_psf_kernels.py | 1 + tests/test_telescope_utils.py | 13 +- tests/test_transformer.py | 7 +- tests/test_units.py | 6 +- tests/test_utils.py | 15 +- 92 files changed, 1107 insertions(+), 2877 deletions(-) diff --git a/notebooks/debug_spectra_lookup.ipynb b/notebooks/debug_spectra_lookup.ipynb index 7bf87a42..07c5d291 100644 --- a/notebooks/debug_spectra_lookup.ipynb +++ b/notebooks/debug_spectra_lookup.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "edac2421", + "id": "0", "metadata": {}, "outputs": [], "source": [ @@ -18,27 +18,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "c5ba1ca6", + "execution_count": null, + "id": "1", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-08 23:07:50,850 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-05-08 23:07:50,853 - rubix - INFO - Rubix version: 0.0.post417+g76e9abf.d20250424\n", - "2025-05-08 23:07:50,853 - rubix - INFO - JAX version: 0.6.0\n", - "2025-05-08 23:07:50,854 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -111,8 +94,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "5a85bfe9", + "execution_count": null, + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -140,8 +123,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "e857c3de", + "execution_count": null, + "id": "3", "metadata": {}, "outputs": [], "source": [ @@ -151,8 +134,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "3f181ff3", + "execution_count": null, + "id": "4", "metadata": {}, "outputs": [], "source": [ @@ -161,192 +144,30 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "8016babb", + "execution_count": null, + "id": "5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-08 23:08:16,661 - rubix - DEBUG - Method not defined, using default method: cubic\n" - ] - } - ], + "outputs": [], "source": [ "lookup_interpolation = get_lookup_interpolation(config)" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "0622cce9", + "execution_count": null, + "id": "6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lookup_interpolation Partial(>, method='cubic', x=Array([4.4904351e-05, 1.4200003e-04, 2.5251572e-04, 4.4904352e-04,\n", - 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" ...,\n", - " [3.19179711e-22, 1.78760657e-21, 5.73130869e-21, ...,\n", - " 1.93975841e-20, 1.86948069e-20, 1.80162264e-20],\n", - " [6.14501020e-11, 8.57527313e-11, 1.14455764e-10, ...,\n", - " 2.07055004e-20, 1.99535222e-20, 1.92279652e-20],\n", - " [1.24949378e-12, 2.30248633e-12, 3.44144765e-12, ...,\n", - " 2.00280460e-20, 1.93006602e-20, 1.85987619e-20]]], dtype=float32), extrap=0)\n" - ] - } - ], + "outputs": [], "source": [ "print(\"lookup_interpolation\", lookup_interpolation)" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "fe481d17", + "execution_count": null, + "id": "7", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-08 23:08:20.694754: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:652] The byte size of input/output arguments (9621985660) exceeds the base limit (8654290944). This indicates an error in the calculation!\n", - "2025-05-08 23:08:20.703903: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3021] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 8.94GiB (9597600124 bytes), down from 8.94GiB (9597600124 bytes) originally\n", - "2025-05-08 23:08:31.169703: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_0_bfc) ran out of memory trying to allocate 8.93GiB (rounded to 9590400000)requested by op \n", - "2025-05-08 23:08:31.169909: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] **__________________________________________________________________________________________________\n", - "E0508 23:08:31.169937 2740412 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes. [tf-allocator-allocation-error='']\n", - "2025-05-08 23:08:31.170372: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_1_bfc) ran out of memory trying to allocate 8.93GiB (rounded to 9590400000)requested by op \n", - "2025-05-08 23:08:31.170537: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *___________________________________________________________________________________________________\n", - "E0508 23:08:31.170558 2740415 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes. [tf-allocator-allocation-error='']\n", - "2025-05-08 23:08:31.171601: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_2_bfc) ran out of memory trying to allocate 8.93GiB (rounded to 9590400000)requested by op \n", - "2025-05-08 23:08:31.171784: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *___________________________________________________________________________________________________\n", - "E0508 23:08:31.171805 2740418 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes. [tf-allocator-allocation-error='']\n", - "2025-05-08 23:08:31.173120: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_3_bfc) ran out of memory trying to allocate 8.93GiB (rounded to 9590400000)requested by op \n", - "2025-05-08 23:08:31.173458: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *___________________________________________________________________________________________________\n", - "E0508 23:08:31.173499 2740421 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes. [tf-allocator-allocation-error='']\n" - ] - }, - { - "ename": "XlaRuntimeError", - "evalue": "RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes.: while running replica 0 and partition 0 of a replicated computation (other replicas may have failed as well).", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mXlaRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m age, metallicity \u001b[38;5;241m=\u001b[39m age_metallicity\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lookup_interpolation(age, metallicity)\n\u001b[0;32m----> 5\u001b[0m interpolation \u001b[38;5;241m=\u001b[39m \u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlookup_interpolation_lax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetallicity\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", - " \u001b[0;31m[... skipping hidden 14 frame]\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/rubix/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py:1298\u001b[0m, in \u001b[0;36mExecuteReplicated.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_token_bufs(result_token_bufs, sharded_runtime_token)\n\u001b[1;32m 1297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1298\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxla_executable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute_sharded\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_bufs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dispatch\u001b[38;5;241m.\u001b[39mneeds_check_special():\n\u001b[1;32m 1301\u001b[0m out_arrays \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mdisassemble_into_single_device_arrays()\n", - "\u001b[0;31mXlaRuntimeError\u001b[0m: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 9590400000 bytes.: while running replica 0 and partition 0 of a replicated computation (other replicas may have failed as well)." - ] - } - ], + "outputs": [], "source": [ "def lookup_interpolation_lax(age_metallicity):\n", " age, metallicity = age_metallicity\n", @@ -357,24 +178,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "47236b6c", + "execution_count": null, + "id": "8", "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'lookup_interpolation_lax' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m _, interpolation \u001b[38;5;241m=\u001b[39m \u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcarry\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlookup_interpolation_lax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetallicity\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - " \u001b[0;31m[... skipping hidden 10 frame]\u001b[0m\n", - "Cell \u001b[0;32mIn[4], line 2\u001b[0m, in \u001b[0;36m\u001b[0;34m(carry, x)\u001b[0m\n\u001b[1;32m 1\u001b[0m _, interpolation \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mlax\u001b[38;5;241m.\u001b[39mscan(\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m carry, x: (carry, \u001b[43mlookup_interpolation_lax\u001b[49m(x)),\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 4\u001b[0m (age, metallicity),\n\u001b[1;32m 5\u001b[0m )\n", - "\u001b[0;31mNameError\u001b[0m: name 'lookup_interpolation_lax' is not defined" - ] - } - ], + "outputs": [], "source": [ "_, interpolation = jax.lax.scan(\n", " lambda carry, x: (carry, lookup_interpolation_lax(x)),\n", @@ -386,7 +193,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ef96f19f", + "id": "9", "metadata": {}, "outputs": [], "source": [] @@ -394,20 +201,9 @@ { "cell_type": "code", "execution_count": null, - "id": "94e29597", + "id": "10", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[CudaDevice(id=0), CudaDevice(id=1)]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\n" ] @@ -415,7 +211,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8b0c1c99", + "id": "11", "metadata": {}, "outputs": [], "source": [] diff --git a/notebooks/filter_curves.ipynb b/notebooks/filter_curves.ipynb index 8618a02c..57a27bc9 100644 --- a/notebooks/filter_curves.ipynb +++ b/notebooks/filter_curves.ipynb @@ -11,24 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-11-07 14:44:55,517 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> < \n", - "/_/|_|\\____/____/___/_/|_| \n", - " \n", - "\n", - "2024-11-07 14:44:55,517 - rubix - INFO - Rubix version: 0.0.post101+gda5b92f.d20241101\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.telescope.filters import load_filter, print_filter_list, print_filter_list_info, print_filter_property" @@ -47,29 +32,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " filterID \n", - " \n", - "------------------------\n", - "SLOAN/SDSS.uprime_filter\n", - " SLOAN/SDSS.u\n", - " SLOAN/SDSS.g\n", - "SLOAN/SDSS.gprime_filter\n", - " SLOAN/SDSS.r\n", - "SLOAN/SDSS.rprime_filter\n", - " SLOAN/SDSS.i\n", - "SLOAN/SDSS.iprime_filter\n", - " SLOAN/SDSS.z\n", - "SLOAN/SDSS.zprime_filter\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "print_filter_list(\"SLOAN\")" @@ -84,53 +49,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " name dtype unit \n", - "-------------------- ------- ---------------\n", - "FilterProfileService object \n", - " filterID object \n", - " WavelengthUnit object \n", - " WavelengthUCD object \n", - " PhotSystem object \n", - " DetectorType object \n", - " Band object \n", - " Instrument object \n", - " Facility object \n", - " ProfileReference object \n", - "CalibrationReference object \n", - " Description object \n", - " Comments object \n", - " WavelengthRef float64 AA\n", - " WavelengthMean float64 AA\n", - " WavelengthEff float64 AA\n", - " WavelengthMin float64 AA\n", - " WavelengthMax float64 AA\n", - " WidthEff float64 AA\n", - " WavelengthCen float64 AA\n", - " WavelengthPivot float64 AA\n", - " WavelengthPeak float64 AA\n", - " WavelengthPhot float64 AA\n", - " FWHM float64 AA\n", - " Fsun float64 erg / (A s cm2)\n", - " PhotCalID object \n", - " MagSys object \n", - " ZeroPoint float64 Jy\n", - " ZeroPointUnit object \n", - " Mag0 float64 \n", - " ZeroPointType object \n", - " AsinhSoft float64 \n", - " TrasmissionCurve object \n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "print_filter_list_info(\"SLOAN\")" @@ -145,44 +66,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", - " AA AA AA AA AA AA AA AA AA AA AA erg / (A s cm2) Jy \n", - "-------------------- ------------ -------------- ------------- ---------- ------------ ---- ---------- -------- ----------------------------------------------------- ----------------------------------------------- ------------------------ -------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- --------------- --------------- --------------- ----------------- ------ -------------- ------------- ---- ------------- --------- ----------------------------------------------------------------\n", - " ivo://svo/fps SLOAN/SDSS.u Angstrom em.wl SDSS 1 SLOAN http://www.sdss.org/dr7/instruments/imager/index.html http://www.sdss.org/DR2/algorithms/fluxcal.html SDSS u full transmission 3556.5239668607 3572.1824003193 3608.0403153219 3055.1091291961 4030.6399499061 540.97112586776 3578.0271197298 3556.5239668607 3680.0 3619.6973042374 565.79845192387 103.21344236463 SLOAN/SDSS.u/Vega Vega 1582.537065543 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=SLOAN/SDSS.u\n" - ] - }, - { - "data": { - "text/html": [ - "Row index=1\n", - "
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" - ], - "text/plain": [ - "\n", - "FilterProfileService filterID WavelengthUnit WavelengthUCD PhotSystem DetectorType Band Instrument Facility ProfileReference CalibrationReference Description Comments WavelengthRef WavelengthMean WavelengthEff WavelengthMin WavelengthMax WidthEff WavelengthCen WavelengthPivot WavelengthPeak WavelengthPhot FWHM Fsun PhotCalID MagSys ZeroPoint ZeroPointUnit Mag0 ZeroPointType AsinhSoft TrasmissionCurve \n", - " AA AA AA AA AA AA AA AA AA AA AA erg / (A s cm2) Jy \n", - " object object object object object object object object object object object object object float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 object object float64 object float64 object float64 object \n", - "-------------------- ----------------- -------------- ------------- ---------- ------------ ------ ---------- -------- ------------------------------------------------------ -------------------- ------------------- ------------------------------------------------------------------ --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -------------- -------------- --------------- --------------- ---------------------- ------ --------------- ------------- ------- ------------- --------- ---------------------------------------------------------------------\n", - " ivo://svo/fps JWST/NIRCam.F070W Angstrom em.wl NIRCam 1 NIRCam JWST https://jwst-docs.stsci.edu/display/JTI/NIRCam+Filters NIRCam F070W filter includes NIRCam optics, DBS, QE and JWST Optical Telescope Element 7039.1194650654 7088.3009369996 6988.4272768359 6048.1970523246 7927.0738659178 1212.8399166581 7099.1873443748 7039.1194650654 7691.5 7022.060805287 1430.8105961315 140.01772043307 JWST/NIRCam.F070W/Vega Vega 2768.4045696982 Jy 0.0 Pogson 0.0 http://svo2.cab.inta-csic.es//theory/fps/fps.php?ID=JWST/NIRCam.F070W" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "print_filter_property(\"JWST\", \"F070W\", \"NIRCam\")" @@ -245,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -256,29 +107,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[SLOAN/SDSS.uprime_filter,\n", - " SLOAN/SDSS.u,\n", - " SLOAN/SDSS.g,\n", - " SLOAN/SDSS.gprime_filter,\n", - " SLOAN/SDSS.r,\n", - " SLOAN/SDSS.rprime_filter,\n", - " SLOAN/SDSS.i,\n", - " SLOAN/SDSS.iprime_filter,\n", - " SLOAN/SDSS.z,\n", - " SLOAN/SDSS.zprime_filter]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "curves.filters" @@ -286,20 +117,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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bUV9nh0UyBTlqedDNGVeNmnr2Ghro7PG0MwktQc2lqp/fQjAJBDWAc4f2s+6zj9FnZ6NzcqbflI/x9g+0GrP7p++JWfkjAJKkomvfF+kc1b/SbEpMTGTDhg3ExcUB0L59eyIjI1Gr1YCpbtr3339PWloabm5uDBo0iDp16lgd4+DBg8pCjr59+9KiRQur/RcvXuT7778nNzcXb29vXnrpJRwdHSvtmiqb69m57E5Kw0mt4mhqJttup3A4NQNDOe/CWpWEt70GraTCzU6Np50GoyxTX2ePWpJwUqtwVavRqCRUQJCDFiMy9ioVnho1ObJMjlEm2FGLr9YOFZBhNOKoUikeG1mWSTUYcVGrSuXFkWW5yHGyOdTnolZjQOZ8ZjaOKhUGGXJkmeRcPfE5udzONXA9O5crWSZPmQzEZ+dikGWuZOUoXrJUQ+n9YhLQUGePv86epk466mnt8LTT4KpRo5GgmZMDDXX22KmEp6q6UtXPbyGYBIL7iCzLIMtIqqK/6SYlxPO/1T8hqVQEt+1I2p3b/Ll4AbIx7+HgUsebwbO+ROfkDMDp/+1m3ewZANQNCuHG+bMAPDtuMsHtOlboNeTk5LBz50727NmDwWBApVLRs2dPOnfuXOBBeefOHb777jtu376Ns7Mzzz33HIGBgRiNRnbs2KHkJz788MM89thjhZ4vISGB5cuXk5qaiqurK+3ataNt27bY2dmh0+lQFfNeVjWyLHMkNZOdd1LZl5zO7qQ0Mop5yNe119DUSUcjRx2edmpu5ejx1drR3NkBlSThoVHjba/hdq4BL3sNDbR2Nh2Kup2rxyDLnM/IJjFXD4BBhmNpmRxISSfbKHM1K4eEnNxSiVIJ8DJ7okIctDRy0tLIUUeAzp4GOnsaOWpt+v2uaqr6+S0Ek0Bwnzi6dRN7VnxPbk42nZ7pS8en+hQQTjlZmSx7702SrycUmB/6cBhhg1/hhw/HknQ9nubdH6HXG2PISkvj27dfJTMlmY7PPEf3F4eydclXHPq/tdQLbsyAf82ukJt8amoqu3bt4siRI2RlZQEQEhJCr169CniO8pOWlsayZcu4ceMGAE2bNiU7O5sLFy4A8NBDD/HYY48Va+PNmzdZvHgxGRkZVtslSaJ+/fq0bNkSPz8/GjRoUC0ElFGW2X47lfmXb7DjTprVPme1CjeNmgdcHenm4UIHV0fq2tvhYacR3o1KQpZNoczzmdlcyMzmTEY2t3L03M7Vk6o3kGE0cio9i2xj8Y9DH3s7HvZwpqHOnoY6e+rr7KmvsyNQp0UjPrtKp6qf30IwCQT3gQtHDrHyX5OstnV4qg89Br5stW3bsq85+PsaHN3cadL5Ic7s20tOViYdnupD52efR1KpuHrqBCsmj0OWjfQe/R6n9uzkzP69eDXwZ9DMz1Fr7MhISear14Zg0OsZ+u/5eDW4twr3d+7c4dtvvyU1NRUAd3d3IiIiaNasWanEWFZWFps2beLQoUPKNrVazZNPPknbtm1LZUN6ejqnTp3i4MGDBVbTWfD29qZLly60bdu2yjwB+5LSGHf6CifSTaJSLcEDLo44qVX8o54HL/h4Ci9FNcQom/LGrmfnkm4wcjYjm7iMLM5kZHM5K4cLmdlkFSGotCqJUCcHAh1MuVJ+WjtCnR2oa28K+fmL1YAVQlU/v4VgEgjuAyumjOfKib9p3v0R6gaGsG3Z10iSipc+W4CHjx9g8i4tHDmYnMxMq1BaYTkhO/67hP1rflF+Vms09Js8A78mocq2n6M/4NLRWMKHjqBdr6fLbXtmZibffPMNiYmJeHl58cQTTxASElIuT87ly5e5ePEiaWlptG7dGj8/vzIfQ5Zl0tPT0el0pKamcuzYMS5cuMClS5fIyTHlvAQFBfH888+j0+nKfPzysi8pjX9fuM72O6nKtmfruvNOkA+NHO+fHYLKIctgZHdSGsfSMrmSlcPlrByuZuVyJTun2DArgLe9hvaujjRzcqC+zo5gBy3e9nYEONijrQYe0ZpCVT+/RSldgaCSuX3tKldO/I1Krebh/oNx8arDxb8OcT72IPvX/MLjr44C4NTeneRkZuLu40vQA+2V+YV9M+3adwA3L5zjwpFDOHvVoecrb1iJJYAGoS24dDSW62fj7sn+tWvXkpiYiKurK0OGDLmnG1XDhg3vuZ+jJEk4O5tytzw8PHj44Yd5+OGHSUtLY9euXcTExHD+/Hm+/vpr+vbti4+Pzz2drzSsu5HEq8cuYATsJIl/1HNnfJAv9XWiFEJtQadW8aiXK496Wf/+G2WZS1k5xKZkcN2csH4xM5tjaZkk6Q0k5Rq4maNnw60UNtyybv6uliDEQUdrFwfamUOzjZ10NHLUohYeqWqHEEwCQSVzZv9eABq2aI2LlynX58E+/Tkfe5Bj2/+kc9QLuNbx5ugfGwFo9UhEsUnhABo7O/pMmMqd+Ku4etdDU0hroDrmVXS3Ll8qt+0XLlzgxIkTqFQqnn/++WrtlXV2duaJJ54gODiYH3/8kcTERJYsWcLw4cOL7DVZEXx9+SYfnjGFCB/xdGFGkwYEOIh2TLaCSpIIdNASWMRnnmUw8ldqBrGpGUp472JmDjdzckk1GDmdkcXpjCx+uX5HmeOkVtHGxZGGOnuaOel4wNWRAJ09Plo7VEJIVRlCMAkEFUB2RjrZGRm41vEusO/i0VgAQjo8qGyr3zSUhi1ac/nYX+xf+wutH32C+DOnUKnVtOjxaKnOKUkSnn4Nitzv3TAQgMSrlzAaDahU6tJfkJmYmBgA2rVrR/369cs8vypo0qQJb7zxBitXruTatWusXr2al156qVKSwedfusHUs9cAGOznxUeN64sQi8AKnVpFJ3dnOrk7W22XZVNdquPpWRxITudEeiY3svWcysgi3WBkT1JagWNpVRL+OnvauDjS3s2JDq6ONHHSid+5+4QQTALBPZKVlsbSd18nPTmJqAnTCGj9gLJPlmVuXjgHgG+jplbzOvfpz+Vjf3F0y0aunzP1VGzUoXORRSnLiqt3XSSVCkNuLhlJSTh7ls3LkpGRwenTpwHo2LFiSxNUNl5eXvTr14///Oc/XL58mZ07d9KjR48KPce5jGymn8sTSzObNBCJvYJSI0kSfjp7/HT2PJYvzGeQZU6nZ3EkNYP47FwOpWQQl5Gl1J+Ky8gmLiNb8UipgDYujjzg6khLZweaOzvQzEkninRWAkIwCQT3yNmD/yPtzm0ADq5fbSWY0u4kkpmagqRS4dXQ32qef8vWipcpPu4UAO2ffLbC7FKp1Th7eJGaeJPUxFtlFkzHjx/HaDRSr149pfp2TcLd3Z1HH32UDRs2sHXrVgICAggMDKyQY5/JyOLpQ3HoZQj3dGFW03vLyxIILKgliVBnB0KdrdsJ6Y0y17JzOJORzcGUdA4mZ3AoNZ0UvZHDqRkcTs0ruaECQhy1tHB2INBBSysXB1q7ONp83a57RQgmgeAeufT3Eav/63NzlZyimxfOA+Dp1wA7+4I5Dr3+OYZVM6aQFH+Nrs8PxK9Jswq1zaWON6mJN0m5dRPfxk1LnpCPEydOANCqVasKtel+8uCDD5KQkEBsbCw7duyoMME041w8t3MNuGpURDeuGaFKQc1Go5Lwd9Di76DlEbNHSpZlrmbn8r+kNP5Oy+R4WhZ/p2WSmKtXPFH5cdeo6ejmRCsXBzq4OtHO1RF3OyEDSot4pwSCe0CWZSvBZMjNJfHyReoFNwJQKm7XDQopdL6LZx0Gz/oSqJzmoZYk89TEm2Wal5uby8WLFwFTTlBNRZIkwsLC+Ouvvzh37hxXrlyhQYOi875Kw/qbSfx+MxmA1W0bEyJKBgiqCEmSaKCzp4GPJ1HmbbJsasR8LC2TE+lZnMvI4khqJifTTav2NiemsDkxb7VeoIM9j3i6EubpwgMujtTVFlxAIjAhBJNAcA/cib9K2u1E1HZ21AtqxLXTJ7h+/myeYLpoyl+qGxBU5DEq00VuyYfKSEku07yLFy+i1+txcXHB27tgIntNwt3dnRYtWnD06FFOnDhxT4LJKMv861w8YMpban5X2EQgqGokSaKe1o56WjvFEwWQYzTyd2omh1IzOJKawb6kdC5m5XAhM4dvr97i26u3APDX2dPYUUc7V0fCvUwiSqzMMyEEk0BwD1w6avIu+TUJpV6wSTBZvEqQF5LzDgyuEvscnF0AyExJKWGkNefPm+wOCQmpFTkPISEhHD16lEuXyl9iAeCLi9c5k5GNq0bFpJCyF90UCKoKe5WKdm5OtHNzUrbdztVzIDmdjbeS2ZeczpmMbC5l5XApK4ctt1P45EICEiYvVBsXRx72cOEBFwca2+jKPCGYBIIyos/NRZaN2NlrlXCcf8s2uNU1JUbfvGgSG9kZGSRdN3kjvIvxMFUmOhfTN8ystLIJpitXrgDg7+9fwsiagaV45c2bNwutnF4aEnP0zL5wHYCpjerjrCl7mQaBoDrhaafh8TpuPF7HDYBUvYHDKRmczsjitxtJ7E9JxyDD+cwczmfmsPpGknmemm4eLgQ5aHnYw5lObk7Y24CAEoJJICgDuVlZ/PeDd7h97QpPvjVOEUwBrR7A3sEUnrl56QKy0cjNSybh5OxVB0dXtyqx18EsmDJTU0sYmYfBYODaNdNy+XvN96kueHl5IUkSWVlZpKWl4eLiUuZj/JhwmxxZprWLAy/4Vl4hTIGgqnDRqOnu6UJ3TxdeaeBNut7AtexcTqVnsTspjcMpGZzJyOJ2roE1ZvH02cXrOKlVNHPS0crFkS7uTjzk7kId+9onL2rfFQkElcjxnX9y67IpGXrtv/8FmLw49UIagQxqOztyszJJupHAjfMl5y9VNg5mYZCVVnrBdPPmTXJzc9FqtdSpU6eyTLuv2NnZ4ebmRlJSErdv3y6zYDLIMkvMOR5D69eO90QgKAknjZrGGjWNnXQ8WdcdMJU3+F+ySTydTM9i2+1UbuXqOZiSwcGUDJZcvYWdJPGCrycfhPjhWos8sUIwCQRl4MKRQwW2hbTrpFTRrtMwkOvn4rh54Vy+FXKN7quN+dFZcphSSx+Ss4Tj6tevXynVsasKd3d3kpKSSE4uWwI8wOGUDC5n5eCqUfFs3YopLCoQ1EQ0KomHPFx4yMN0bzHKMqfSsziVnsWBlHT23EnjeHoWWxJT+KiWldwQgkkgKAOWApMPvzCEA2tXIqlUdHwmStlfNzCI6+fiuHHhPDcuWART1SR8Q55gyk4v2GahKK5fN+Xp+Pr6VopNVYWbmyksmpSUVOa5f5iXYYd7uooKygJBPlT5Cm3+o57py8SeO2lkGI21LjFcCCZBtcaYkcGVN0eRfe4cfjM/xqlTJ6t9CVOnknPhInXHvotjhw6VaktudhbpSaZ2BK0fe4J2kU8jSSqrxreW1XDxZ06ReMW0IqteETWY7geWvCqDXm9VULM4bt401Wyq6eUE7sbd3R2gXB6mjbdMc+7uVC8QCArS1cO55EE1kNol/wS1Cjknhyuj3yJ992708fFce28cxqwsZf+tefNIXrOWzCNHuDzyNXIuX65Ue1Ju3gDA3sERnZMzdvbaAgKkboBJMF06GovRYMDB1Q0Xr6oTHva6vDpBOZkZxYzMo7YKpvJ6mM5lZHMiPQu1BD2FYBIIbBYhmATVCmNODgnT/8WF/i9wrk8f0nfuVPbpExJIXrcOAENKCre/+z5vXloaV995Fzk3t9JsS75pClW51a1X5LJ078Ag1PlEVMMWrau0jpFKrUajNbVkyc3KLHF8RkYG6enpALUm4duCRTCV1cO0zrwaqJu7Cx6ijYRAYLMIwSSoViR+8w13vvuOzNhYcs6cRbK3p+HXX+M9ZgwAKet+M/27/v+Qs7PRNm5Moy1/oHJ1Jeuvv0iYNg3ZYKgU25JvmASTq3fRjWjtdQ4Et+2o/Ny4Y+dKsaUsWLxM2Rkle5hu3TKtBHN1dUWrLdj7riZjEUypZSixALDuZhIAT5lXCQkEAttECCZBtUE2GLjz3x8A8Bg8iHqTPiRo1Uqcuz2M25O9AcjYv5/c69dJ/s3kaXL7xz+wq18fvxn/Akki6edfOPfU04onqiKxCCZLgcqi6PbiEBo2b0Xrx56gSZeHK9yOsmLJY8ophYeptobjABwdHQHIzs5Gr9eXas6FzGz+TstELcETdaqmlpZAIKgeCP+yoNqQceAghlu3ULm5UW/sWKR8oS07Pz8c2rUj89Ahbi9bRuZB0/J+18heALg8+ij1//0p8VOnkXPuHNfGvocxMxOPfv0qzL6UUgomD9/69Js8o8LOe6/YO5iEQm6mbQsmnU6HJEnIskxGRgauriXnI22/bfJGdXJzwqsWFuITCASlR3iYBNWG1I0bAHB57FErsWTBtXckALcXfQuyjEPbttjlW/ruGhlJoy1/4DF4EADXP55JzpWrFWZf/hymmoTFw5RdiqTvO3dMqwA9PT0r1aaqQKVSKV4mS55WSexJMpVjeNi97JXBBQJB7UIIJkGVkLJhI5ff+Cc3//MfjDk5yHo9KRs3AeD6xBOFznHt1QvJ/MAD8HplWIExamdn6o0fj2OHDsgZGdyY+XHF2WzxMBWTw1QdseQw5ZTCw2RZQebhUTuLMzo5mRqPZpQin0uWZUUw1dZl0gKBoPQIH7PgviLLMrfmz+fWF18CkLZlC7nXruEaGYkhMRG1uztOnQtPlNZ4elJ/9r+5vehbnMPCcHn00ULHSSoVPlMmc+7Jp0j9Yws5V65i3+DeKs5mZ6STZS7+6FrjPEzmkFwJOUyyLCseJkvNotqGRTCVxsN0OiObmzl6dCqJdq6OJY4XCAS1G+FhEtw3ZIOBhClTFbHkHB4OQPLKVSRMmQqAyxMRhYbjLLiEhRHw3TK8hr1c7Lm0jRrh1LUryDJ3fvjvPdtuSfh2cHG1qm1UE1BCciV4VTIzM8nJyQFqr2CyhORK42HadScvf6m2VSwWCARlR9wFBPeNhI8+ImnFCpAk6n34AQ3nz8Mtqg8Aueaik+59+lTY+TwGDgAg6ZeVGEsRjiqO5BsJQM3LXwKw0+oA0OdkFzvO4l1ydnbGrhQVwWsillIJ2dnFvxeQl7/0kMhfEggECMEkuE9kHDxI0o8rQKWi/ux/4znAJGbqvvsu2qZNAXAOC8OhdesKO6dzjx7Y1a+PMTmZlPX/d0/HslT5dq3rUxGm3Vc09ubClSWIBEv+Um31LkHpBZMsyxxINoXtHnR3qnS7BAJB9UcIJkGlI8syN+d8BoD7c8/h2quXsk/j4UHgzz8R+NMKGsz9skLPK6nVigcr9Y8/7ulYSddrsofJJBL05nBbUaSkmBrMWgo81kZKK5iuZOdyPUePRoI2LiJ/SSAQCMEkuA9kxMSQceAAkr09dV5/rcB+lb09Dq1bI2kqfg2CyyOPAJAeE2PVh66sWEJy7vVqroeppJBcWpopBOXsXHtXhJVWMFm8Sy2dHXFQi9ukQCAQgklQyeRcuUrC1GkAuPfrh53P/RUc2qZN0fj4IGdmkrFvX7mPk6x4mGqiYLIHSi+YXFxqb86OTmfK5yqtYOrgJrxLAoHAhBBMgkojbedOzj35JDkXLqD29KTOG6/fdxskScK5e3cA0vfsLdcxZKNRKVrpXs+3hNHVD0tIrqQcJkuPNVvwMGWV4G08lGJaRdfeVeQvCQQCE0IwCSoF2Wgk4aNo5KwsHNq3J+C7ZWiqqBiiQ9u2AGT9/Xe55ifdSMCQm4vazg4XrzoVadp9Ic/DVHwOkwjJmcgxGjmWZlpV2VbUXxIIBGZE4UpBpZB96hS5ly6hcnTE/6uFqJyq7pu6Q8sWAGQdP45sMCCp1WWaf/3cGQC8/QNRlXFudUCjLV0Ok8XDVJtDcqURTCfSs8iRZTw0agJ09vfLNIFAUM0RHiZBpZB59CgAutatq1QsAdgHByPpdBgzMpR6T2Xh6sljANQLblzRpt0XNHYlh+T0ej2Z5lpVtu5hijWH49q4OCJJ0n2xSyAQVH+EYBJUCllHTeEvh1Ytq9gSU3kBe39/AHIuXSrTXH1ODnH/2wNAUNsOFW7b/aA0ZQUsrUJUKhUODjWrknlZsBTkzM3NLXJMXIYpv6mZs+6+2CQQCGoGQjAJKoVMc76QrmWrKrbEhH2AWTBduFimeTuWLyY96Q7OXnUIaN22MkyrdEoTksuf8K2qxW1ALILJYDBgNBoLHXMuw/Q+NXIUgkkgEORRe++MgirDmJVF9unTQPXwMAHYBwQAZfMwpdy8QezG3wHo+cobaGpou5DSlBWwhYRvwKrlS1FepnOZpvcp2EF7X2wSCAQ1AyGYBBVO9smTYDCg9vJC41s9luHbNWgIQO6VK6Wec+bA/5BlIw2atyS4XcfKMq3SsStFaxRbSPgG0OQrjlqYYMoxGrmUaQpdhjgKwSQQCPIQq+QEFYL+zh0Spk7D4YE2SGrTr5WuZYtqkzSrqVsXAP3Nm6Wec/1cHAANm1dcf7uqwFLpWzYaMej1qAupqG7JYXKq4gT9ykalUmFnZ0dubm6hguliZg5GwEmtoq69uD0KBII8xB1BUCEkfv0NqRs2kLphA7oWpmX8DtUkfwnKJ5huXjwPQL3gkEqx6X5hyWECU1iuMMGUkWFaGVbbBRNQrGCyhONCHLTVRuwLBILqgQjJCSqEjIMHlP9nHTMtw7cUjKwOaLy9AdAnJiIbDKWak3LrBlAz26HkJ79AKmqlnEUw1eYVchaKWyl31pzwHSzCcQKB4C6EYBJUCPqE6wW2OTzQpgosKRyNlydIEhgMGO7cKXF8TlYm2eYwlYuXd2WbV6lIkqSIJoNeX+gYSw0mR8faX9m6OMF0TggmgUBQBEIwCe4ZWa8vEOpyeugh1NVoxZWk0aB2dwdAf/t2ieNTE28BYO/giLYWiAi1ZTm9vvCVYcLDZOJspqkGU4hYIScQCO5C5DAJ7hn9rVtgNIJGg/9XC0nZtIk6I0ZUtVkFULu6YrhzB6N5RVhxWARTTewdVxhqjR2QiaGIpfQWwSQ8TBYPk6jBJBAIrBGCSXDP6K+bwnGaut44de2KU9euVWxR4ahcXQEwJKeUODb9jskL5ezpVak23S8UD1MRgkmE5CBNb+B6jilkGewgesgJBAJrREhOcM/kmvOX7OpV7+RotVkwGVNLFkzZ6aZCjjqn6hNWvBeKC8np9XpyzMngthySs6yQq2Onwc1OfJcUCATWCMEkuGf01xMA0PjUq2JLikflairKaEgpOSSXZU741jrWjmX2anNtrMI8TJZwnCRJ6HS1PxRVpGAyh+NEwUqBQFAYQjAJ7hnFw1S3egsmtasbAIaU5BLH5mSaBVMtqUtUXEjOEo7T6XS1uo+chaIEk6WkQJBI+BYIBIVQ+++OgkpHn2DxMFX3kJzJw2RMKUVIzux1qTUeJqXpbMGyAraU8A15ginnrppUStFK4WESCASFIASToFwkr1vHrYVfIRuN5JqTvu2qe0jO7C0ymgVCcWRnmDxM9rVERJhWyRXvYbI1wVSUh0nUYBIIBIUhMhsFZSb3+g2ujX0PAF3z5nkepmqe9K0yJzQbMzJLHJtd23KYignJ2VINJshrwKu/q4jnebOHKViE5AQCQSEID5OgzGSfPqX8P+PAAXJvmFqIVHcPk2QRTJmlEEy1LCSnMQsmfSGr5GwtJFeYYErTG0jWm1rmNNSJkgICgaAgVS6Y5s2bR1BQEDqdjvbt27Nz585ixy9fvpw2bdrg6OiIr68vL730EomJiffJWgFAzoWLyv8zDx+G3FyQJKVfW3VF5WASBMbMkkNyStJ3LREReSG5gjlMWVmm6ta27GG6lm0Skq4aFc4adZXYJRAIqjdVGpJbsWIFb731FvPmzeOhhx5i4cKF9OrVi+PHj+Pv719g/K5duxg8eDBz5szhqaee4urVq4wcOZJXXnmFX3/9tQquwDbRmz1KABn79gGgqVMHyezFqK6oHE2CQC5NSK6WeZiKC8lZBJNWaxuhKItgMpibMBsMBuLT0migkgnWapT3QyAQ3H/s7e2r7WrdKhVMs2fPZtiwYbzyyisAfPbZZ2zcuJH58+czY8aMAuNjYmIIDAxk1KhRAAQFBfHqq68ya9as+2q3rWMopPBjdV8hB/lymEoTkjMXrqw1gklpvltQMGVnm3J3bKEGE1h7mOLj40lKSsJRb+Bfrip0Kj3nz5+vYgsFAttFpVIRFBSEvX31C41XmWDKycnh4MGDjB8/3mr7448/zp49ewqd07VrV95//33Wr19Pr169uHHjBr/88gu9e/e+HyYLzBgLKfxo5+dXBZaUjdLmMOlzcjCYwzW1pg6TpuhK3xaPiq0JJg8PD5KSkqhbty7OGjvscg24adT4ihwmgaBKMBqNXLt2jfj4ePz9/ZEkqapNsqLKBNOtW7cwGAzUq2edKFyvXj0SzKuu7qZr164sX76c559/nqysLPR6PU8//TRffvllkefJzs5WvkEDpJSiBo+geAyFNK+tCYJJ5WjJYSpeMFlKCiBJ2OtqR15PXkiuYA6T5e/DlkJyGo0GLy8v6tati5eXFxmZ2UiSAQetHTpt9Q4tCwS1GW9vb65du4Zer1dKgFQXqjxQeLeClGW5SFV5/PhxRo0axaRJkzh48CAbNmzg/PnzjBw5ssjjz5gxAzc3N+XVsGHDCrXfFjHWVMHkYMlhKj7p25K/ZK9zQKqmsfSyUlwvOVv0MGm1WlQqlbIyMEeWAbCvZt9oBQJbwxKKs+QYVieq7GlQp04d1Gp1AW/SjRs3CnidLMyYMYOHHnqIsWPH0rp1ayIiIpg3bx7ffvst8fHxhc6ZMGECycnJyuvy5csVfi22hsXD5PDAA8o2XbOmVWRN6cmfwySbH5CFkZ1Ru/KXoPjClbbmYVKr1cqXMsu/uUbT74OdSggmgaAqqW5huPxUmWCyt7enffv2bN682Wr75s2b6dq1a6FzMjIyCmTPq9WmJcBFPQC1Wi2urq5WL8G9YWkt4vHiC2Bnh8bPF13r1lVsVclI5rICyDJyvjDt3Sgr5GpJ/hKA2q7o5ru26GGCvHuGLMvkmv9vV41v1gKBoGqp0njDmDFj+Oabb/j22285ceIEb7/9NpcuXVJCbBMmTGDw4MHK+KeeeopVq1Yxf/58zp07x+7duxk1ahSdOnXCrwaEhGoLhjSTB8ahXTsabdpI8K+/oqoB3gmVQ54gKC6PKSejdtVggqKTvg0Gg9IixFY8TBbBZMEgg9nBJDxMgvtKYGAgn332WZXakJCQQM+ePXFycsLd3R0weXlWr14NwIULF5AkidjY2CqzsbpQpYLp+eef57PPPmPatGk88MAD7Nixg/Xr1xMQEABAfHw8ly5dUsYPHTqU2bNnM3fuXFq2bEnfvn1p2rQpq1atqqpLsDnknBxks9hQu7hg5+uL2s2tiq0qHZJaDeZcnlJ5mGphSE5/l4cp/4IIW/UwWbxLagnUNcDDdOPGDV599VX8/f3RarX4+PgQERHB3r17gZIfwpcvX2bYsGH4+flhb29PQEAAo0ePLrIA8IgRI1Cr1fz4448F9k2ZMgVJkgrkkcbGxiJJEhcuXLDafvHiRbRaLSkpKaSnpzNu3DiCg4PR6XR4e3sTFhbGb7/9powPCwtDkiQkSUKr1VK/fn3li/PdbN26lfDwcDw9PXF0dKRx48YMGTLEqkDpwoULadOmjSIO2rZty8yZM4t8ryqb/fv3M2LEiCo7P8CcOXOIj48nNjaW06dPA6Znb69evQodv23bNiRJIikp6T5aWT2o8l5yr7/+Oq+//nqh+5YsWVJg25tvvsmbb75ZyVZVP2RZJj3HgLO2aj8yi3cJQOXsXIWWlA+VvT3G3NziBZO5BpO9Qy3yMBVRuNISjtNoNEp4u7Zzt4dJCcfVEO9SVFQUubm5LF26lODgYK5fv86WLVu4fft2iXPPnTtHly5daNKkCT/88ANBQUEcO3aMsWPH8n//93/ExMTg6empjM/IyGDFihWMHTuWRYsW0b9//wLH1Ol0LFq0iDFjxtCkSZNiz79mzRrCwsJwdXVl0KBB7Nu3j7lz59K8eXMSExPZs2dPAeE2fPhwpk2bRm5uLlevXuXXX3+lf//+DB06lK+++gqAY8eO0atXL0aNGsWXX36Jg4MDcXFx/PLLLxiNRgDFxi+++IIePXqQnZ3NX3/9xfHjx0t83yqanJwc7O3t8a4G3RHOnj1L+/btady4sbLN5z7U1ZNlGYPBUODvsVoj2xjJyckyICcnJ1e1KWUi+rdjcsiE3+Vtp25UqR3Z58/Lx5s2k0+2a1+ldpSXU527yMebNpOzTp8ucsyuFd/Jn/brLW/+Zt59tKxy+evPjfKn/XrLK2dMttp+7do1efLkyfInn3xSNYZVASkpKfInn3wi79mzR87MzJRvZefIscnp8pm0TDk9O/e+v4xGY6ltv3PnjgzI27ZtK3JMQECAPGfOnEL3PfHEE3KDBg3kjIwMq+3x8fGyo6OjPHLkSKvtS5YskTt37iwnJSXJDg4O8vnz5632T548WW7Tpo3cs2dPuW/fvsr2w4cPy0CB8Y888og8d+5cWZZl2c3NTV6yZEmx19ujRw959OjRBbZ/++23MiBv3rxZlmVZnjNnjhwYGFjssZ555hl56NChxY65m8WLF8tubm5W23799Vc5/6PT8h4sWLBAbtCggezg4CA/99xz8p07d5QxQ4YMkZ955hn5X//6l+zr6ysHBATIslzwswLkBQsWyL1795YdHBzkZs2ayXv27JHj4uLkHj16yI6OjnLnzp3lM2fOWNm0du1auV27drJWq5WDgoLkKVOmyLm5uSVeX0BAgAworyFDhih2/Prrr7Isy/L58+dlQD58+LDy/8LmGI1GeebMmXJQUJCs0+nk1q1byz///LNyrq1bt8qAvGHDBrl9+/aynZ2d/OeffxawKTMzUz5+/LicmZlZYF9VP79rkLSzbb7eaao+vHD7WXo0qbpvJYZUk/dFVUOT5yVzno4xO6fIMdm1OIfJeNdSXVtL+AZrD5OcL+HboJdp/tHG+27P8WkRONqX7lbs7OyMs7Mzq1evpnPnzmXKO7t9+zYbN25k+vTpBfoG+vj4MGDAAFasWMG8efOUlUqLFi1i4MCBuLm5ERkZyeLFi5k6dWqBY3/88cd07NiR/fv307Fjx0LPn5SUxM6dO5XIgY+PD+vXr6dPnz64uLiU+joAhgwZwjvvvMOqVat47LHH8PHxIT4+nh07dtC9e/dC5/j4+LB9+3YuXryopH1UFGfOnOGnn35i3bp1pKSkMGzYMN544w2WL1+ujNmyZQuurq5s3ry52FW6H330EbNnz2b27NmMGzeOF198keDgYCZMmIC/vz8vv/wy//znP/m///s/ADZu3MjAgQP54osv6NatG2fPnlXCfJMnTy7W7v379zN48GBcXV35/PPPS+wn2bBhQ1auXElUVBSnTp3C1dVVmfPBBx8oOcaNGzdmx44dDBw4EG9vb3r06KEc47333uPTTz8lODhYyZmqKdSOIjM2xJU7pvyhtGx9sX90lYXR3BZFXcYbXHVB0ppqfMg5RYfkcmphDpPKHG4z6q0LV9paSQHAKvQoyzJ6JeG7igwqAxqNhiVLlrB06VLc3d156KGHmDhxIn/99VeJc+Pi4pBlmdDQ0EL3h4aGcufOHW7evKmMj4mJ4fnnnwdg4MCBLF68WAlx5addu3b069evQOeG/Kxfv55WrVoptfC++uor9uzZg5eXFx07duTtt99m9+7dJV4HmNpnNGnSRMmR6tu3Ly+88AI9evTA19eXZ599lrlz51oVKp48eTLu7u4EBgbStGlThg4dyk8//VTo9ZSVrKwsli5dygMPPED37t358ssv+fHHH63K5jg5OfHNN9/QokULWrZsWeSxXnrpJfr160eTJk0YN24cFy5cYMCAAURERBAaGsro0aPZtm2bMn769OmMHz+eIUOGEBwcTM+ePfnoo49YuHBhiXZ7e3uj1WpxcHDAx8cHtxLyUdVqtRKyrVu3rjInPT2d2bNn8+233xIREUFwcDBDhw5l4MCBBeyYNm0aPXv2JCQkBC8vrxJtrE4ID1MNQG/I+4POyjVw5HISfebvoV+HBszoc3+X8xvMbVFUNVQwqewtgqloD1NWusXDVHsEU14vOWvBJDxMslKDydlezfFpEffdHge7suWORUVF0bt3b3bu3MnevXvZsGEDs2bN4ptvvmHo0KHltsPyBSy/dykiIoI6deoAEBkZybBhw/jjjz94/PHHC8yPjo4mNDSUTZs2Ubdu3QL716xZw9NPP6383L17d86dO0dMTAy7d+/mzz//5PPPP2fq1Kl8+OGHpbLXYqtarWbx4sVER0fz559/EhMTw/Tp05k5cyb79u3D19cXX19f9u7dy99//8327dvZs2cPQ4YM4ZtvvmHDhg331PDV39+fBg0aKD936dIFo9HIqVOnlHygVq1alao/Wut8JVosNQlbtWpltS0rK4uUlBRcXV05ePAg+/fvZ/r06coYg8FAVlYWGRkZSnHWyuT48eNkZWXRs2dPq+05OTm0bdvWaluHDh0q3Z7KogZ8pxKkZOU95DJzDSzdewGDUeaHffe/CKcxzSSYaqyHyd7kSSku6bs2lhVQqU0iwWgQHiaVSqU8aE0eJnOVb5UKR3vNfX+Vp1CfTqejZ8+eTJo0iT179jB06NASwy+NGjVCkqQik5xPnjyJh4cHderUwWAwsGzZMn7//XellYyjoyO3b99m0aJFhc4PCQlh+PDhjB8/voD3Ozc3lw0bNvDMM89Ybbezs6Nbt26MHz+eTZs2MW3aND766CNyivlCAyZBEBcXR1BQkNX2+vXrM2jQIP7zn/8oD/EFCxZYjWnZsqUSLtu8eTObN29m+/bthZ5HpVIVei0lcXdhVDB5mEpD/nYglvmFbbN4xoxGI1OnTiU2NlZ5HT16lLi4uPv2Rchiy++//25lx/Hjx/nll1+sxpb2faiOCA9TDSA5M+8PNDVLz7WkvBpCOXoj9prK072yLHM9ejq5V67g9+9P8zxMrjVVMJm+4RmLuSHXzrICwsOUn/xeJksOk6YGlBQoiubNmyt1c4rCy8uLnj17Mm/ePN5++22rfJWEhASWL1/O4MGDkSSJ9evXk5qayuHDh61CmCdPnmTAgAEkJiYWGk6ZNGkSISEhBUoQbN26FXd3dx7I1x2gqOvQ6/VkZWUV641ZunQpd+7cISoqqsgxHh4e+Pr6km72GBd1PqDIMd7e3qSmppKenq486AurR3Tp0iWuXbum1APcu3evEjasbNq1a8epU6do1KhRpZ8LCm9d0rx5c7RaLZcuXbLKV6ptCMFUTcnKNbD5+HXCmnpbCSaA+OQs5f93MnKo51p5D7us48e5Y05cTPl9PQZLDpNzDRVMWouHqRjBlGm6edrXIsGU52GyTvq2RQ8TWHcI0GMWTDWgrEBiYiJ9+/bl5ZdfpnXr1ri4uHDgwAFmzZpl5b25evVqgQe7v78/c+fOpWvXrkRERBAdHW1VVqB+/fpKWGfRokX07t2bNm3aWB2jRYsWvPXWW3z//feMHj26gH316tVjzJgxfPLJJ1bb165daxWOA1ONpRdeeIEOHTrg5eXF8ePHmThxIuHh4VYdGTIyMkhISECv13P16lVWrVrFnDlzeO211wgPDwdM9ZViY2N59tlnCQkJISsri2XLlnHs2DGlOftrr72Gn58fjzzyCA0aNCA+Pp7o6Gi8vb3p0qULAL/++isTJkzg5MmTADz44IM4OjoyceJE3nzzTfbt21douRudTseQIUP49NNPSUlJYdSoUfTr1+++LM+fNGkSTz75JA0bNqRv376oVCr++usvjh49SnR0dIWfLyAgAEmS+O2334iMjMTBwQEXFxfeffdd3n77bYxGIw8//DApKSns2bMHZ2dnhgwZUuF2VAUiJFdN+femU7z5w2GifztRQDBdTMxrHns7vXjX9b2S9fexfP8/itHiYXKrqavkzDlMxdZhMgkmXQ12Hd+NxcN0d0jOEvqwVcFklGUsEZea4GFydnbmwQcfZM6cOXTv3p2WLVvy4YcfMnz4cObOnauM+/TTT2nbtq3Va+3atTRu3JgDBw4QEhLC888/T0hICCNGjCA8PJy9e/fi6enJ9evX+f333wv13kiSRJ8+fYoMywGMHTsW57tqtK1du7ZAOC4iIoKlS5fy+OOPExoayptvvklERAQ//fST1bivv/4aX19fQkJCePbZZzl+/Liyms9Cp06dSEtLY+TIkbRo0YIePXoQExPD6tWrFY/HY489RkxMDH379qVJkyZERUWh0+nYsmWL4i1LTk7m1KlTynE9PT35/vvvlYT1H374gSlTphS45kaNGtGnTx8iIyN5/PHHadmypZV9lUlERAS//fYbmzdvpmPHjnTu3JnZs2dX+EpAC/Xr12fq1KmMHz+eevXq8c9//hMwre6bNGkSM2bMIDQ0lIiICNatW1cgbFqTkeSqWGpVhaSkpODm5kZycnK17isXOP535f9fvNCWUT8cLnTcyte60j7Ao9LsSJg2jTv//QEAx06d0PjUI2XtOuq+9x5eL79UaeetLK68+Sapm//AZ8pkPAopwifLMnNefAbZaGTE/CW4eNapAisrnvgzp/jv++/g6l2X4XO/VbavWrWKv/76i8cff7zIHo61ka+//pqWLVvSpHkLrkgaVBK0cqk9OWvViUOHDvHII49w8+ZNq1yc2sKUKVNYvXq1aB1SQWRlZXH+/HmCgoIKpApU9fNbhORqAMkZRXuRMnL0Re6rCLJO5n3byo2PR2VOhFbX2Bym4pO+9dnZyOYExtqVw2Suw6Qv3MNUmtU7tQnLiii9LIMkmu5WJnq9ni+//LJWiiWBbSFCctWQbL11nsmVpKIbxaZnG4rcd6/Iskz2mTPKz7kJCRjMdU1ULtXXO1ccJSV9W4pWSioVdtrakwhtqcN0d9K3rQomS0hOXwsSvqs7nTp1YtCgQVVthk2yfPlypdjp3a8WLVpUtXk1DuFhqobcTLX2fpy/WfQqj8zcyvMw6W/exJiv8Bu5ueScN1Ucr7EeJiWHqXjBpHV0Ktdy7+pKUUnftiqYLB4mgyV/qQYkfAuqJ1OmTCk0r6k68PTTT/Pggw8Wuk94/MqOEEzVkOspdwmmW6aHuKO9mowc6wdeZXqYcs6dA8A+IABjVhb669cx3LkD1FwPk8qySq4ED1NtqsEERZcVsFXBZPEwGSyNd2uROBYILLi4uJS57YygaERIrhpyMzXL6ucLiaaHeIi3c4GxlZnDlH3mLAD2ISHY3bU8tsZ6mOyLXyVnqcFUm0oKAKg05lVhRaySszXBpHiYzD+LkJxAICgJIZiqIXd7mHLNcYNg74IP8cr0MGWfNeUvaUNC0Pj6Wu2rqa1RLEnfxiJ6ydVeD1Ne8938C2NtVTApOUzmn4VgEggEJSFCctWQG3d5mCwE1ynoYcrKrcSQnNnDpG0Ugpy/HYAk1eDWKMX3kstW+sgVfK9rMqp81ZqNBr0ioGxVMN3tYbITOUwCgaAEhIepGnIjpXDvR0jdPA+T2nyDz6xEwZRtyWEKaYTGp17euT09kTQ1U2uXPum7tnmY8j4vo3kVpsFgQG/OabI1waTkMJl/1gi9JBAISkAIpmrIDfMqucZ1rb0c+T1MTeuZPDx3J4FXFPo7dzAkJgKgDQ7CzicvJKfx9q6Uc94PJLNnRdYXnvuVk1n7+shB3io5yEv8zt9E1FYFk9EcnRRJ3wKBoCSEYKqGWARTqK/1SjQfNx0TejXjqTZ+PP2AqcljZXmYLOUDNH6+qBwdsfPNS/rW1Km51a8l81JauYiO44qHqRa1RYGCITnIC8epVCqrBqu2gEqlQs4nktRCMAkEghIQgqkaYlkl18w3L09IksDNwY5Xe4Tw5Qtt8XA0PfizKsnDlHPxEmAqKQBg17Chsk9Tr26lnPN+YAklyvoiBJMlh8mhdoXkJElSvEwWD1P+/KXaVHOqNKhUKmRM16yWJFQ16Ppv3LjBq6++ir+/P1qtFh8fHyIiIti7dy8AgYGBfPbZZ0XOv3z5MsOGDcPPzw97e3sCAgIYPXo0iWaP8t2MGDECtVrNjz/+WGDflClTkCSJkSNHWm2PjY1FkiQuXLhgtf3ixYtotVpSUlJIT09n3LhxBAcHo9Pp8Pb2JiwsjN9++00ZHxYWhiRJSJKEVqulfv36PPXUU6xataqALVu3biU8PBxPT08cHR1p3LgxQ4YMUcLOYGrS26ZNG5ycnHB3d6dt27bMnDmzyPdKIMiPEEzVDL3BSKK5oW5+D5Orzk7JWwLQ2Zk8ApXmYbp4AcgTTBpPTxw6tDfZ8kSvSjnn/UCyM4emiij4afEw1bayAlCwtICtJnyDtYfJrobdBaOiojhy5AhLly7l9OnTrF27lrCwMG7fvl3i3HPnztGhQwdOnz7NDz/8wJkzZ1iwYAFbtmyhS5cuBY6RkZHBihUrGDt2bJENd3U6HYsWLeL06dMlnn/NmjWEhYXh6urKyJEjWb16NXPnzuXkyZNs2LCBqKioAsJt+PDhxMfHc+bMGVauXEnz5s3p378/I0aMUMYcO3aMXr160bFjR3bs2MHRo0eVdixGc6ujRYsWMWbMGEaNGsWRI0fYvXs37733HmlpaSXaLRCAWCVX7biRmo0smyoPN8pXd8nNwboqq0MlC6bcS2YPk39ex2v/r74iN+E62uCa23265JBc7cxhAlPitz47u1APk62hVqsxmgVTTSopkJSUxK5du9i2bRs9evQAICAggE6dOpVq/htvvIG9vT2bNm3CwcEBAH9/f9q2bUtISAjvv/8+8+fPV8b//PPPNG/enAkTJuDr68uFCxcIDAy0OmbTpk2pW7cuH3zwAT/99FOx51+zZg19+vQBYN26dXz++edERkYCJs9Y+/btC8xxdHTEx1wHrmHDhnTu3JlmzZrx8ssv069fPx577DE2b96Mr68vs2bNUuaFhITwxBNPKD+vW7eOfv36MWzYMGVbSe1BlixZwltvvUVSUpKybfXq1Tz77LPYWN96AcLDVO24mGh6YDfwcMDbRats12qsPyoHe7NgqqSQXO6NGwBWuUsqR8caLZYAUEJyRSR919IcJsjXHkUIJrOHyfR/JeFbliEn/f6/yvDgtfQBW716NdlFFF8titu3b7Nx40Zef/11RSxZ8PHxYcCAAaxYscJKCCxatIiBAwfi5uZGZGQkixcvLvTYH3/8MStXrmT//v1Fnj8pKYmdO3fy9NNPK+dcv349qampZboOgCFDhuDh4aGE5nx8fIiPj2fHjh1FzvHx8SEmJoaLFy+W+XwCAQgPU7Xjormqd4CXkxJ2A7BT3yWYzPsqqw6TIdHkmld7eVXK8asKJYepCA9TVi0tKwAF26PYsmBSq9XI5u+LiocpNwP+5Xf/jZl4DexLJ9A1Gg1Llixh+PDhLFiwgHbt2tGjRw/69+9P69ati50bFxeHLMuEhoYWuj80NJQ7d+5w8+ZN6tatS1xcHDExMYooGThwIKNGjWLy5MlKHSsL7dq1o1+/fowfP54tW7YUevz169fTqlUrGprzIb/66isGDBiAl5cXbdq04eGHH+a5557joYceKvF9UKlUNGnSRMmR6tu3Lxs3bqRHjx74+PjQuXNnHn30UQYPHoyrqym1YfLkyfTp04fAwECaNGlCly5diIyM5LnnnitwPQJBYYjfkmrG6eumeHpQHdMNdFBnU0hs1KONrMZZPEyVVlbAnMugqW2Cyc5ch6lID1PtDcnd3YDXlgVTfg9TTQrJgSmH6dq1a6xdu5aIiAi2bdtGu3btWLJkyT0d1+JZsiwAWLRoEREREdQxr4qNjIwkPT2dP/74o9D50dHR7Ny5k02bNhW6f82aNYp3CaB79+6cO3eOLVu2EBUVxbFjx+jWrRsfffRRqe212KpWq1m8eDFXrlxh1qxZ+Pn5MX36dFq0aEF8fDwAvr6+7N27l6NHjzJq1Chyc3MZMmQITzzxhJLnJBAUh/AwVTP2XzAJlbb+7gB8+GRzXu0RTAMPa49HZeYwyTk5GJOTAVORytpEcavkZKOR7FpahwnyeZhE0jdqtRq9JYfJspjCztHk7bnf2JXdm6nT6ejZsyc9e/Zk0qRJvPLKK0yePJmhQ4cWOadRo0ZIksTx48f5xz/+UWD/yZMn8fDwoE6dOhgMBpYtW0ZCQgKafEVPDQYDixYt4vHHHy8wPyQkhOHDhzN+/PgCCeK5ubls2LCBCRMmWG23s7OjW7dudOvWjfHjxxMdHc20adMYN25csb+XBoOBuLg4OnbsaLW9fv36DBo0iEGDBhEdHU2TJk1YsGABU6dOVca0bNmSli1b8sYbb7Br1y66devG9u3bCQ8PL3AelUpVIFcptwjvtKD2IwRTNSIlK5dj10xCpVOQSajYa1QFxBLkeZgqIySnv5Nk+o9ajdrNrcKPX5VYVskVFpLLycpS8klqo2Cy1GISOUzWZQUUD5MklTo0Vt1o3rw5q1evLnaMl5cXPXv2ZN68ebz99ttWeUwJCQksX76cwYMHI0mSklt0+PBhqxpdJ0+eZMCAASQmJuJViPd50qRJhISEFChBsHXrVtzd3XnggQdKvA69Xk9WVlaxv5dLly7lzp07REVFFTnGw8MDX19f0s2lQoo6H1DkGG9vb1JTU0lPT8fJnNcYGxtb7DUIai9CMFUjDl28g1GGhp4O+Lo5FDvW4mHKNcjkGowFcpzuBcNt07JetacHUi2L7VtWyRVWViA7wxQOVWs0aGqhiFAa8ArBZMphUlbJVbExZSAxMZG+ffvy8ssv07p1a1xcXDhw4ACzZs3imWeeUcZdvXq1wIPd39+fuXPn0rVrVyIiIoiOjiYoKIhjx44xduxY6tevz/Tp0wFTOK537960adPG6hgtWrTgrbfe4vvvv2f06NEF7KtXrx5jxozhk08+sdq+du1aq3AcmGosvfDCC3To0AEvLy+OHz/OxIkTCQ8PV/KOwFTaICEhAb1ez9WrV1m1ahVz5szhtddeU7xCCxcuJDY2lmeffZaQkBCysrJYtmwZx44d48svvwTgtddew8/Pj0ceeYQGDRoQHx9PdHQ03t7edOnSBYBff/2VCRMmcPLkSQAefPBBHB0dmThxIm+++Sb79u2759CnoOZSu56GNZzDl5IA6BBQchgsf0J4RXuZ9OaEb41n7cpfgvwhucIEkykcVxtrMEFeHSYRkrPOYapJVb6dnZ158MEHmTNnDt27d6dly5Z8+OGHDB8+nLlz5yrjPv30U9q2bWv1Wrt2LY0bN+bAgQOEhITw/PPPExISwogRIwgPD2fv3r14enpy/fp1fv/990K9N5Ik0adPnyJrMgGMHTsWZ2frtk5r1661EnQAERERLF26lMcff5zQ0FDefPNNIiIiCpQm+Prrr/H19SUkJIRnn32W48ePs2LFCubNm6eM6dSpE2lpaYwcOZIWLVrQo0cPYmJiWL16tVJ+4bHHHiMmJoa+ffvSpEkToqKi0Ol0bNmyRfGWJScnc+rUKeW4np6efP/990rC+g8//MCUKVNK+JQEtRVJtrFiEikpKbi5uZGcnGz1LaY6MPjbfew4fZNpz7RgcJfAYsfKskzwxPXIMux7/1HquugqzI7ktWu59t44nLp2wf/bbyvsuNWBrNOnOf/0M6g9PGiyd4/Vvisnj7Fi8jjcfXwZ9vnXVWRh5fHDpPe4duo4T4+ZSOMHu7J69WpiY2N57LHHePjhh6vavPtKzMFDpKjU1G3oTysv9xolmmoahw4d4pFHHuHmzZvYWTy8AkERZGVlcf78eYKCgtDprJ9rVf38Fh6maoLRKBN76Q4A7fw9ShwvSVJeaYGcil3hYVkhp/aoXQnfkK9wZSEeprwVcs4F9tUG8soKmPK3LB4mW3yIZUp5tz5xE6xc9Hq9UnVbIKjJiBymasLF2xmkZOnRalQ09XEpeQLgaK8mI8dARhFtPsqLwVzVtrYlfEPxlb6z0005TLWxBhPkS/oWZQXIlFRokFEh21wfvftNp06dSl2JXCCozogvV9WEk/EpADT1cSl1ArfST66CazEpgsndvUKPWx0oTQ5TbVwhBwULV1qaktriN/8Ms0iSbCsjQSAQ3ANCMFUTTpgFU7NSepeg8moxGZLMNZhqo2CyiAO9vkB9lbzGu7XVw2QpXGktmPLX2bEV0s23PpXQSwKBoJQIwVRNOJFg6qcU6lv6RLbKqsVkSE4CQO3hXqHHrQ5I+cXBXWE5S9FKXS3sIwf5PUym3xdLAT7bFExmDxNCMQkEgtIhBFM14fwtk3ejcd3Se5jyQnIVm/RtEx4mCoblLDlM9g61UzCpNJbmuyahZNMeJrNOEiE5gUBQWoRgqgbIskx8UiYAfu6lLw9QeSG5JKCWCqZ84qCAYLKVHCZz0rct5zClmXWSSggmgUBQSoRgqgakZOlJNydul1ThOz+O9pUsmGrhKjnye5juCsnlmHOYtLU0JHd3axRbDsmlKR4m0XRVIBCUDiGYqgHxySbvkrujnZKXVBry6jBVnGAyZmcjZ5rsqZUeJkmCIlbK5XmYRNJ3bSfNaFJMIodJIBCUFiGYqgE3U7MBqFfGat06s7jKqEDBZPEuoVajcil9PlVNQiktcHfSt8XDVNtDcqKsAGkGk1ASq+QEAkFpEYKpGpCWZXpwuejK9k2/MnKY8ofjamtBv6IEU47Zs2bvUEs9TPkEk8FgwGg0haNs0cOUYr52jDUvJHfjxg1effVV/P390Wq1+Pj4EBERwd69ewEIDAzks88+K3L+5cuXGTZsGH5+ftjb2xMQEMDo0aNJTEwsdPyIESNQq9X8+OOPBfZNmTIFSZIYOXKk1fbY2FgkSeLChQtW2y9evIhWqyUlJYX09HTGjRtHcHAwOp0Ob29vwsLC+O2335TxYWFhSJKEJElotVrq16/PU089xapVqwrYsnXrVsLDw/H09MTR0ZHGjRszZMgQ5YsBmJr0tmnTBicnJ9zd3Wnbti0zZ84s8r0SCPIjBFM1IC3b9AftpC2fYKrIsgL6m7cA0HjVvsa7FvLXYspPTpYpJGenq7i+fNUJtVLpW2/1ELFFwWTxMNXEHKaoqCiOHDnC0qVLOX36NGvXriUsLIzb5pZGxXHu3Dk6dOjA6dOn+eGHHzhz5gwLFixgy5YtdOnSpcAxMjIyWLFiBWPHji2y4a5Op2PRokWcPn26xPOvWbOGsLAwXF1dGTlyJKtXr2bu3LmcPHmSDRs2EBUVVUC4DR8+nPj4eM6cOcPKlStp3rw5/fv3Z8SIEcqYY8eO0atXLzp27MiOHTs4evSo0o7F8sVg0aJFjBkzhlGjRnHkyBF2797Ne++9R1paWol2CwQgWqNUC9LNgsm5rILJvuIrfetv3ABAU7duhR2zulGYh0mW5XweptIn3tck8soKGGxaMBlkmTSjEVChkmVk2dQeRZZlMvWZ990eB41Dqb25SUlJ7Nq1i23bttGjRw8AAgICSt165I033sDe3p5NmzbhYP499/f3p23btoSEhPD+++8zf/58ZfzPP/9M8+bNmTBhAr6+vly4cIHAwECrYzZt2pS6devywQcf8NNPPxV7/jVr1tCnTx8A1q1bx+eff05kZCRg8oy1b9++wBxHR0d8fHwAaNiwIZ07d6ZZs2a8/PLL9OvXj8cee4zNmzfj6+vLrFmzlHkhISE88cQTys/r1q2jX79+DBs2TNnWokWLEt+ztWvX8s4773DlyhU6d+7M0KFDGTp0KHfu3MG9FuZ5CorGtu6U1RTLCjknbekTviFfHaZcA9l6AwajjKP9vX2kimCqV++ejlOtsSuY9K3PzUE2fxO119XSkJzaUlZAr6yQU6vVqFS25WhOyjUoqd75ZUqmPpMH//vgfbfnfy/+D0e70v3OOTs74+zszOrVq+ncuTNarbbU57l9+zYbN25k+vTpiliy4OPjw4ABA1ixYgXz5s1TBNyiRYsYOHAgbm5uREZGsnjxYqZOnVrg2B9//DEdO3Zk//79dOzYsdDzJyUlsXPnTpYsWaKcc/369fTp0weXMuZLDhkyhHfeeYdVq1bx2GOP4ePjQ3x8PDt27KB79+6FzvHx8WH79u1cvHiRgICAUp3nwoULPPfcc4wePZpXXnmFw4cP8+6775bJVkHtwbbulNWU8obkLGUFUrNyeerLXXSftY2kjJx7skV/4zoAmrre93Sc6ozSgDefYMrNzPMs2Nf6kJzBplfI3TFfuyTLSFCgRU51RqPRsGTJEpYuXYq7uzsPPfQQEydO5K+//ipxblxcHLIsExoaWuj+0NBQ7ty5w82bN5XxMTExPP/88wAMHDiQxYsXKyGu/LRr145+/foxfvz4Is+/fv16WrVqRcOGDQH46quv2LNnD15eXnTs2JG3336b3bt3l3gdACqViiZNmig5Un379uWFF16gR48e+Pr68uyzzzJ37lxSUlKUOZMnT8bd3Z3AwECaNm3K0KFD+emnnwq9HgsLFiygadOmfPLJJzRt2pT+/fszdOjQUtkoqH3Y3t2yGlLukJzZw3T0agq30kwr7Q5cuMNjzcvvHcox34Ds6tcv9zGqO5LGLJhy8kJyOVlZANhpdUi11OOSF5LT2/QKuds5ZsFk/tkimBw0Dvzvxf/dd3scNGULAUdFRdG7d2927tzJ3r172bBhA7NmzeKbb765p4e55X3I712KiIigTp06AERGRjJs2DD++OMPHn/88QLzo6OjCQ0NZdOmTdQtJKS/Zs0ann76aeXn7t27c+7cOWJiYti9ezd//vknn3/+OVOnTuXDDz8slb0WW9VqNYsXLyY6Opo///yTmJgYpk+fzsyZM9m3bx++vr74+vqyd+9e/v77b7Zv386ePXsYMmQI33zzDRs2bCjU03rq1KkCHrPShj8FtY/a+WSoYZTXw2QJyVnEEuTVdCoPsiyTdfKU6djNmpX7ONUdqZA6TDnmPnK1NX8J8tdhsnUPkykEfndbFEmScLRzvO+v8qxG1el09OzZk0mTJrFnzx6GDh3K5MmTi53TqFEjJEni+PHjhe4/efIkHh4e1KlTB4PBwLJly/j999/RaDRoNBocHR25fft2kcnfISEhDB8+nPHjxxfw2uXm5rJhwwaeeeYZq+12dnZ069aN8ePHs2nTJqZNm8ZHH31ETk7xnnKDwUBcXBxBQUFW2+vXr8+gQYP4z3/+w/Hjx8nKymLBggVWY1q2bMkbb7zB8uXL2bx5M5s3b2b79u2Fnie/KMu/TWCbCMFUDUgv7yq5Qopc3kwrf0hOf/Mmhtu3QaVC27hxuY9T3clL+s57r3KyanfCN+Svw5Rr01W+b+daPEymB19teAA2b96c9PT0Ysd4eXnRs2dP5s2bR2am9RerhIQEli9fzvPPP48kSaxfv57U1FQOHz5MbGys8vr5559ZvXp1kSUIJk2axOnTpwuUINi6dSvu7u488MADJV6HXq8ny+zxLYqlS5dy584doqKiihzj4eGBr69vse9L8+bNAYoc06xZM/bv32+17cCBA8XaJqi92N7dshqSnm36xutcxqRvS0guP/m9TWUl+5TJu2QfGIiqlubxQL5+cvlyFyw5THba2iuYVCKHCTAlfQNINVAnJSYm0rdvX15++WVat26Ni4sLBw4cYNasWVbem6tXrxIbG2s119/fn7lz59K1a1ciIiKIjo4mKCiIY8eOMXbsWOrXr8/06dMBUziud+/etGnTxuoYLVq04K233uL7779n9OjRBeyrV68eY8aM4ZNPPrHavnbtWqtwHJhqLL3wwgt06NABLy8vjh8/zsSJEwkPD8fV1VUZl5GRQUJCAnq9nqtXr7Jq1SrmzJnDa6+9Rnh4OGCqrxQbG8uzzz5LSEgIWVlZLFu2jGPHjvHll18C8Nprr+Hn58cjjzxCgwYNiI+PJzo6Gm9vb7p06QLAr7/+yoQJEzh58iQAr776KrNnz2bcuHEMGzaM2NhYJWm9ttapExSN8DBVA5SQXBlXuBUmmFKz9IWMLB3ZZ88C1GrvEpDXGiU3X0jOBjxMSg6TwbZzmFIsIbka6GFydnbmwQcfZM6cOXTv3p2WLVvy4YcfMnz4cObOnauM+/TTT2nbtq3Va+3atTRu3JgDBw4QEhLC888/T0hICCNGjCA8PJy9e/fi6enJ9evX+f333wv13kiSRJ8+fYoMywGMHTsWZ2dnq21r164tEI6LiIhg6dKlPP7444SGhvLmm28SERFRoDTB119/ja+vLyEhITz77LMcP35cWc1noVOnTqSlpTFy5EhatGhBjx49iImJYfXq1Ur5hccee4yYmBj69u1LkyZNiIqKQqfTsWXLFrzMdeeSk5M5Zf7iCBAUFMQvv/zCqlWraN26NfPnz+f9998HKNMKRUHtwPa+XlZDlKTvslb6ti+ody3HKg855y8AYB8UWO5j1ASUkJwhfw5T7RdMllVyBr3BpkNyyYpgqnlotVpmzJjBjBkzihxzd3XtuwkICGDx4sVF7q9Xr57y+1EYX3zxhfL/KVOmMGXKFKv9Li4uyko7gEOHDpGSkqIIFwsTJkxgwoQJxdq6bdu2YvdbaNu2Ld99912xY6KioooN4QFKjaX8PP3001besenTp9OgQQN0tdgLLygc27tbVkPKX7iy4Pi0exFM5hut9q5EytqGZBYOGPIKfloEk52u9gqm/M13bTkkl3JX0ndN8jDVRPR6vVJ1uyYyb948OnbsiJeXF7t37+aTTz7hn//8Z1WbJagCbO9uWQ2519Yo+bkXD5Pe/K1Q4+Nb7mPUCDSm9806JGcDq+Q0BXOYaupD7F5IMgsmkY9wf+jUqVONXoofFxdHdHQ0t2/fxt/fn3feeadEz5igdiIEUxUjy7JS6bu8dZjycy+CyXDnDgBqD/dyH6MmoNRhKiwkV4s9TGq16boNer1Nh+RS7grJCQ+ToDjmzJnDnDlzqtoMQTVAfMmqYrL1RgxG0w27rB4mrabgx5eWXb6+crLBgCE5GQCNh0e5jlFTKCwkl2sTSd9mD5PetkNyyXclfQsEAkFpEIKpismfc+RYiMeoOFSqgmmr5fUwGVJSlGX26treULLQkJy50nct9jAVVrjSFkNyybnCwyQQCMqOEExVjFK00l5dqAAqiW6NTW0LuoaYlsVm5howGsv+ADDcSQJA5eys9FqrreSF5PJ7mEyCqbb2kYN8q+QMth2Su3uVnBBMAoGgNFS5YJo3bx5BQUHodDrat2/Pzp07ix2fnZ3N+++/T0BAAFqtlpCQEL799tv7ZG3FU96EbwuTn2rOiO7BRP+jpbItS1/2sJwhOQkAtZtbueyoSeSF5PI8THpz1W+Nfe2trZJXh8l2C1fmGI1kmj2pVX7zEwgENYoqvVuuWLGCt956i3nz5vHQQw+xcOFCevXqxfHjx/H39y90Tr9+/bh+/TqLFi2iUaNG3LhxQ7n510TyqnyX76NoVNeFiZGhSh4UQFauEUf7sh1HNntYVI6O5bKjRmEJyeX7vdFnmyqka+zL+MbVIJRK3zbcfDdFn1fdXXiYBAJBWahSwTR79myGDRvGK6+8AsBnn33Gxo0bmT9/fqGF2TZs2MD27ds5d+4cnp6eAAQGBt5Pkyuc8vaRuxu1SsJerSLHYCQrt+weJmOmSTBJtTgkZSGv+W7e+6TPsQim2uthyuslZ7tJ38nm63YqpDO9QCAQFEeV3TVycnI4ePAgjz/+uNX2xx9/nD179hQ6Z+3atXTo0IFZs2ZRv359mjRpwrvvvlugkWRNIi8kV7aE78LQ2pk+zvIIJjnb7GGyBcGkLljpO9cmPEx5ITlbzWGy5C85a1RKLzDhYRJUFWFhYbz11ltVakNGRgZRUVG4uroiSRJJSUkEBgby2WefKWMkSWL16tVVZmN1ocoE061btzAYDNSrV89qe7169UhISCh0zrlz59i1axd///03v/76K5999hm//PILb7zxRpHnyc7OJiUlxepVnShvle/C0JlX2WXei4fJwQYEkzkkR34PkzmHya4W94fKK1xpux4mSw0mZ/W9f0GpKm7cuMGrr76Kv78/Wq0WHx8fIiIi2Lt3L0CBh93dXL58mWHDhuHn54e9vT0BAQGMHj2axMTEQsePGDECtVrNjz/+WGDflClTkCSJkSNHWm2PjY1FkqQCbVouXryIVqslJSWF9PR0xo0bR3BwMDqdDm9vb8LCwvjtt9+U8WFhYUiShCRJaLVa6tevz1NPPcWqVasK2LJ161bCw8Px9PTE0dGRxo0bM2TIEKuUjYULF9KmTRucnJxwd3enbdu2zJw5s8j3qrJZtWoVH330UZWdH2Dp0qXs3LmTPXv2EB8fj5ubG/v372fEiBGFjr9w4QKSJBVo7mwLVLlf+u6Oz7IsF9kF2mg0IkkSy5cvp1OnTkRGRjJ79myWLFlSpJdpxowZuLm5Ka+GDRtW+DXcC/ea9J0fneJhMpYwsiBGcx0ilbb2Cyal+W7+HKac2p/0rRZlBUgyf5lwqcEepqioKI4cOcLSpUs5ffo0a9euJSwsjNu3b5c499y5c3To0IHTp0/zww8/cObMGRYsWMCWLVvo0qVLgWNkZGSwYsUKxo4dW2TDXZ1Ox6JFizh9+nSJ51+zZg1hYWG4uroycuRIVq9ezdy5czl58iQbNmwgKiqqgHAbPnw48fHxnDlzhpUrV9K8eXP69+9v9UA/duwYvXr1omPHjuzYsYOjR48q7ViM5iT/RYsWMWbMGEaNGsWRI0fYvXs37733HmlpaSXaXdFYPLyenp64uLjc9/Pn5+zZs4SGhtKyZUt8fHyQJAlvb28c70M+a3E9C6sjVSaY6tSpg1qtLuBNunHjRgGvkwVfX1/q16+PW76VXKGhociyzJUrVwqdM2HCBJKTk5XX5cuXK+4iKgBL0neFCCazByG7PCE5W/IwFRKSs42k77zfMVsNyVk8TC6agh4mWZYxZmTc91dZBFtSUhK7du1i5syZhIeHExAQQKdOnZgwYQK9e/cucf4bb7yBvb09mzZtokePHvj7+9OrVy/++OMPrl69yvvvv281/ueff6Z58+ZMmDCB3bt3F9rYt2nTpoSHh/PBBx+UeP41a9YojWzXrVvHxIkTiYyMJDAwkPbt2/Pmm28yZMgQqzmOjo74+PjQsGFDOnfuzMyZM1m4cCFff/01f/zxBwCbN2/G19eXWbNm0bJlS0JCQnjiiSf45ptvsDf/Ta9bt45+/foxbNgwGjVqRIsWLXjhhRdK9PCsXbuWxo0b4+DgQHh4OEuXLlVCVwBLlizB3d2d1atX06RJE3Q6HT179rR61kyZMoUHHniAb7/9luDgYLRaLbIsFwjJBQYGEh0dzeDBg3F2diYgIIA1a9Zw8+ZNnnnmGZydnWnVqhUHDhywsnHPnj10794dBwcHGjZsyKhRo0hPTy/x8wgLC+Pf//43O3bsQJIkwsLCFDuK8lIGmXuNtm3b1moOwOLFiwkNDUWn09GsWTPmzZun7LN4pn766SfCwsLQ6XR8//33JdpYnagywWRvb0/79u3ZvHmz1fbNmzfTtWvXQuc89NBDXLt2zeobwenTp1GpVDRo0KDQOVqtFldXV6tXdSI9p+JCcg72podAecoKGJUcptpbuNFCoSE5G0j6VuUTCbYqmCw5TE7qvFufRbDImZmcatf+vr/kMuRgOjs74+zszOrVq8k2i/zScvv2bTZu3Mjrr7+Ow10V7X18fBgwYAArVqywEnCLFi1i4MCBuLm5ERkZyeLFiws99scff8zKlSvZv39/kedPSkpi586dimDy8fFh/fr1pKamluk6AIYMGYKHh4cSmvPx8SE+Pp4dO3YUOcfHx4eYmBguXrxY6vNcuHCB5557jn/84x/Exsby6quvFhCVYPLETZ8+naVLl7J7925SUlLo37+/1ZgzZ87w008/sXLlymLDWXPmzOGhhx7i8OHD9O7dm0GDBjF48GAGDhzIoUOHaNSoEYMHD1Y+p6NHjxIREUGfPn3466+/WLFiBbt27SpVg+BVq1YxfPhwunTpQnx8fKGhzrvZt28fAH/88YfVnK+//pr333+f6dOnc+LECf71r3/x4YcfsnTpUqv548aNY9SoUZw4cYKIiIgSz1edqNKQ3JgxY/jmm2/49ttvOXHiBG+//TaXLl1S4uETJkxg8ODByvgXX3wRLy8vXnrpJY4fP86OHTsYO3YsL7/8coEbQE0hNasCc5jMD8TyhOQsHiaVDXiY7g7JGY0GDJacnlrsYVLnE0d6s2CytZCc4mFSq4sM/VdnNBoNS5YsYenSpbi7u/PQQw8xceJE/vrrrxLnxsXFIcsyoaGhhe4PDQ3lzp073DQ34Y6LiyMmJobnn38egIEDB7J48WIlxJWfdu3a0a9fP8aPH1/k+devX0+rVq2UtIivvvqKPXv24OXlRceOHXn77bfZvXt3idcBoFKpaNKkieLx6tu3Ly+88AI9evTA19eXZ599lrlz51rlrE6ePBl3d3cCAwNp2rQpQ4cO5aeffir0eiwsWLCApk2b8sknn9C0aVP69+/P0KFDC4zLzc1l7ty5dOnShfbt27N06VL27NmjiAswLXT67rvvaNu2La1bty7y9y8yMpJXX32Vxo0bM2nSJFJTU+nYsSN9+/alSZMmjBs3jhMnTnD9+nUAPvnkE1588UXeeustGjduTNeuXfniiy9YtmwZWeZyMUVhyfeyt7fHx8dHWX1eHN7e3gB4eXlZzfnoo4/497//TZ8+fQgKCqJPnz68/fbbLFy40Gr+W2+9pYzx8/Mr8XzViSr9evn888+TmJjItGnTiI+Pp2XLlqxfv56AgAAA4uPjuXTpkjLe2dmZzZs38+abb9KhQwe8vLzo168f0dHRVXUJ90xaBSZ9W1bJZeaUw8Nk/sOSbCCH6e6QnCV/CWp50nf+kJyNJn0nFxKSs3xTlxwcaHro4H23SSrjl72oqCh69+7Nzp072bt3Lxs2bGDWrFl88803hT7MS4vyPpgf5IsWLSIiIoI6dUzdBCIjIxk2bBh//PFHgdXNANHR0YSGhrJp0ybq1q1bYH/+cBxA9+7dOXfuHDExMezevZs///yTzz//nKlTp/Lhhx+Wyl6LrWq1msWLFxMdHc2ff/5JTEwM06dPZ+bMmezbtw9fX198fX3Zu3cvf//9N9u3b2fPnj0MGTKEb775hg0bNqAqpNTEqVOn6Nixo9W2Tp06FRin0Wjo0KGD8nOzZs1wd3fnxIkTyviAgABFbBRH69atlf9b0lNatWpVYNuNGzfw8fHh4MGDnDlzhuXLl1u9N0ajkfPnzxcpkCuSmzdvKosJhg8frmzX6/VWKTSA1ftU06jyu+Xrr7/O66+/Xui+JUuWFNjWrFmzAmG8mkxalumbvrOu4lbJlSckpxSutAEP090hufyCSWNXez1MkiQhqVTIRmOeR81GBVP+VXL5hYJUQwq3WvJkevbsyaRJk3jllVeYPHlysYKpUaNGSJLE8ePH+cc//lFg/8mTJ/Hw8KBOnToYDAaWLVtGQkKC1e+IwWBg0aJFhQqmkJAQhg8fzvjx4wskiOfm5rJhwwYmTJhgtd3Ozo5u3brRrVs3xo8fT3R0NNOmTWPcuHFK7lFhGAwG4uLiCoiZ+vXrM2jQIAYNGkR0dDRNmjRhwYIFTJ06VRnTsmVLWrZsyRtvvMGuXbvo1q0b27dvJzw8vMB5CluEVFTOWWEeo/zbnJycirye/OT3+lrmF7bN4hkzGo28+uqrjBo1qsCxiioAXdFYbPn666958MEHrfap71qRWtr3oTpS7rvlzp07WbhwIWfPnuWXX36hfv36fPfddwQFBfHwww9XpI21GouHybUCBJODXflDcoqHySZymKxDckrCt509Ui0vaKhWa9AbcxQPk62G5JzVqhoZkiuK5s2bl1gnx8vLi549ezJv3jzefvttqzSGhIQEli9fzuDBg5EkScktOnz4sNUD7+TJkwwYMIDExES8vLwKnGPSpEmEhIQUKEGwdetW3N3deeCBB0q8Dr1eT1ZWVrGCaenSpdy5c4eoqKgix3h4eODr61ts8nPz5s0BihzTrFkz1q9fb7Xt7oRrMHlSDhw4oHiTTp06RVJSEs2aNSvy3BVFu3btOHbsGI0aNar0cwHK52LI14uzXr161K9fn3PnzjFgwID7YkdVUK6nw8qVK4mIiMDBwYHDhw8ryYepqan861//qlADazt5OUz3/uDS3UvhSktZARvwMKGE5EzvU25O7V8hZ0GlUSODzdZhspQVcLUr6GGqCSQmJvLII4/w/fff89dff3H+/Hl+/vlnZs2axTPPPKOMu3r1KrGxsVav27dvM3fuXLKzs4mIiGDHjh1cvnyZDRs20LNnT+rXr8/06dMBUziud+/etGnTRvHItGzZkqioKLy9vYtc3VSvXj3GjBnDF198YbV97dq1VuE4MK3QWrhwIQcPHuTChQusX7+eiRMnEh4ebrU4JyMjg4SEBK5cucL//vc/xo0bx8iRI3nttdcUr9DChQt57bXX2LRpE2fPnuXYsWOMGzeOY8eO8dRTTwHw2muv8dFHH7F7924uXrxITEwMgwcPxtvbmy5dugDw66+/WomcV199lZMnTzJu3DhOnz7NTz/9pEQ+8gtuOzs73nzzTf73v/9x6NAhXnrpJTp37lxo+K6iGTduHHv37uWNN94gNjaWuLg41q5dy5tvvlkp56tbty4ODg5s2LCB69evk5ycDJhWAs6YMYPPP/+c06dPc/ToURYvXszs2bMrxY6qoFyCKTo6mgULFvD1119bfUPt2rUrhw4dqjDjbAFFMFVkSO5eClfaQg6Txrr5ri2UFLBgymPKu9HbmmBKKWSVXE3C2dmZBx98kDlz5tC9e3datmzJhx9+yPDhw5k7d64y7tNPP6Vt27ZWL8vy+AMHDhASEsLzzz9PSEgII0aMIDw8nL179+Lp6cn169f5/fffC/XeSJJEnz59iqzJBDB27FicnZ2ttq1du9ZK0AFERESwdOlSHn/8cUJDQ3nzzTeJiIjgp59+shr39ddf4+vrS0hICM8++yzHjx9nxYoVVkvWO3XqRFpaGiNHjqRFixb06NGDmJgYVq9eTY8ePQB47LHHiImJUZKno6Ki0Ol0bNmyRfGWJScnc+rUKeW4QUFB/PLLL6xatYrWrVszf/58ZZWcNl++o6OjI+PGjePFF1+kS5cuODg4FFroszJo3bo127dvJy4ujm7dutG2bVs+/PBDfH19K+V8Go2GL774goULF+Ln56d8rq+88grffPMNS5YsoVWrVvTo0YMlS5YoZQhqA5Jcjq9Xjo6OHD9+nMDAQFxcXDhy5AjBwcGcO3eO5s2bl5iZX5WkpKTg5uZGcnJytSgx0GbqJpIzc/ljTA8a1XUueUIx/Gv9Cb7acY7h3YJ4v3fzMs29OHAQGQcOUP+zObg+8cQ92VHdSfrlF+I/+BDnsDAaLpjP1ZPH+XHye7j7+DLs86+r2rxKZcGrg0hLSSGtaVsAPvjgA5sSTS13/c2tXD1/tA4k99J5vLy8cHFxwcPDo6pNq7UcOnSIRx55hJs3b9aKEPD06dNZsGCBUmdpyZIlvPXWW0pdJsG9kZWVxfnz5wkKCkJ3V6uuqn5+l+trlq+vL2fOnCmwfdeuXQQHB9+zUbaCLMtKDpNLhXqY7iWHqfZ7mO4OydlClW8LKrUGOV8o4e6EzNqMLMt5OUya2pXDVJ3R6/VK1e2ayLx589i/fz/nzp3ju+++45NPPilQXFNgG5TrKf3qq68yevRovv32WyRJ4tq1a+zdu5d3332XSZMmVbSNtZbUbD0Go8nB5+ZQtTlMSmsUm0j6tg7JWXKY7GxBMGnUYE5s12g0NiUaMo0yOWaHuotaTZJ5e03KYaqJdOrU6b7k8lQWcXFxREdHc/v2bfz9/XnnnXcKrParruzcuZNevXoVub8q2sLUZMolmN577z2Sk5MJDw8nKyuL7t27o9Vqeffdd0tVXVRgIiHZ5NVxd7RTvEP3glK4Un8PhSt1tV80KKvkci11mGwrh0mW8gSTLWHxLqkAx0IqfQsEhTFnzhzmzJlT5P6hQ4feU/2ryqRDhw422SS3sij3HXP69Om8//77HD9+HKPRSPPmzQsk+gmKxyKYfFwrJgxmEV3lKlxpTnwuaxG9Gok5DKWE5Gwo6VutVkMhtV1sAUsNJjeNqcq3LXnXBLaJg4PDfSs3YAvc01IRR0dHOnToQLNmzfjjjz84ceJERdllEyiCya2iBJPp48wuT+HKTEtIrvbnMCkeprsqfdtKDlP+kJwtkWz2KLre1XhXeJgEAkFpKJdg6tevn7KENTMzk44dO9KvXz9at27NypUrK9TA2kx8BXuYHO6lrIANFq7E/AA16E3V1tU24HFRa2w3JKd4mMx/J8LDJBAIykK5BNOOHTvo1q0bYCr0ZTQaSUpK4osvvqjRfd3uNwkpJpFSr6JCcvbmkFwZBZOcmwvm8JQt5DAVCMnl2o5gyp/0bWshuZR8Ibn8CA+TQCAoDeUSTMnJyUqH4g0bNhAVFYWjoyO9e/cmLi6uQg2szSQkm8JgvhUUknMoZw6TMV/dLFvIYbq7+a7BLJg0NiAgbDnpO8ksmO4OyQkEAkFpKJdgatiwIXv37iU9PZ0NGzYojRjv3LlToNCUoGgSUkzJxvUqWDCVtQ6T0Zy/hEqFZAOiQbKzDskZzcJJZQMCQpUv6dvWBJPFw+QuPEwCgaAclOuO+dZbbzFgwACcnZ0JCAggLCwMMIXqWrVqVZH21Woq3MNU3pBcvqKVtpDXIRUVktPUfrGo1miQbTQkZ8lhctGIHCaBQFB2yuVhev3119m7dy/ffvstu3btQmW+AQcHB4scplKSozdyJ8P0oK7nUrEepowcfZnmWUJytrBCDihQ6dtobkRrC4JJpdaAjYbk0sz1yWr6KrkbN27w6quv4u/vj1arxcfHh4iICPbu3QtAYGAgn332WZHzL1++zLBhw/Dz88Pe3p6AgABGjx5NYmJioeNHjBiBWq0utDfalClTkCSJkSNHWm2PjY1FkiQuXLhgtf3ixYtotVpSUlJIT09n3LhxBAcHo9Pp8Pb2JiwsjN9++00ZHxYWppSA0Gq11K9fn6eeeopVq1YVsGXr1q2Eh4fj6emJo6MjjRs3ZsiQIUqjaTA16W3Tpg1OTk64u7vTtm1bZs6cWeR7JRDkp9xlBTp06MCzzz5rVXupd+/ePPTQQxViWG3ndrppKbtaJVVIlW/I8zBl5RoxGkv/EJBtTDBZQnKyeXWcQUn6rv0CQmXDq+RSzQLZpYa3g4mKiuLIkSMsXbqU06dPs3btWsLCwrh9+3aJc8+dO0eHDh04ffo0P/zwA2fOnGHBggVs2bKFLl26FDhGRkYGK1asYOzYsUU23NXpdCxatIjTp0+XeP41a9YQFhaGq6srI0eOZPXq1cydO5eTJ08q+bB3C7fhw4cTHx/PmTNnWLlyJc2bN6d///6MGDFCGXPs2DF69epFx44d2bFjB0ePHlXasRiNJqG8aNEixowZw6hRozhy5Ai7d+/mvffeK1O1a1mWrQSYwLYo1x3TYDCwZMkStmzZwo0bN5RfSAt//vlnhRhXm0lMN+UveTjao1JVTGjAIV+18Gy9URFQJWE0V/m2hYRvyAvJYQ7RKGUFbMDDpFarQWWbhStTlJCcSTBaQnI1ycOUlJTErl272LZtGz169AAgICCg1K1H3njjDezt7dm0aRMO5r93f39/2rZtS0hICO+//z7z589Xxv/88880b96cCRMm4Ovry4ULFwgMDLQ6ZtOmTalbty4ffPABP/30U7HnX7NmDX369AFg3bp1fP7550RGRgImz1j79u0LzHF0dMTHxwcw5c927tyZZs2a8fLLL9OvXz8ee+wxNm/ejK+vL7NmzVLmhYSE8ES+RuLr1q2jX79+DBs2TNnWokWLYu3dtm0b4eHhbNiwgffff5+//vqLjRs3Eh4eXuw8Qe2kXB6m0aNHM3r0aAwGAy1btqRNmzZWL0HJWDxMXk4VV106f3uVsoTlZEsfOa0NlBSAAiE5gwjJ2QSWkJxLESE5WZbJzTbc91dZBJuzszPOzs6sXr2abHOF+tJy+/ZtNm7cyOuvv66IJQs+Pj4MGDCAFStWWNmzaNEiBg4ciJubG5GRkSxevLjQY3/88cesXLmS/fv3F3n+pKQkdu7cydNPP62cc/369aSmppbpOgCGDBmCh4eHEprz8fEhPj6eHTt2FDnHx8eHmJgYLl68WObzvffee8yYMYMTJ07QunXrMs8X1A7Kdcf88ccf+emnn5RvBoKyYxFMnhUomNQqCa1GRbbeWKbEb2OWDbVFIX9IzrqsgK3UYZJttNJ3SSE5fY6Rr0Zvv58mATDi8x7YaUvnDdZoNCxZsoThw4ezYMEC2rVrR48ePejfv3+JD/K4uDhkWSY0NLTQ/aGhody5c4ebN29St25d4uLiiImJUUTJwIEDGTVqFJMnT1byVi20a9eOfv36MX78eLZs2VLo8devX0+rVq1o2LAhAF999RUDBgzAy8uLNm3a8PDDD/Pcc8+VKq1DpVLRpEkTJUeqb9++bNy4kR49euDj40Pnzp159NFHGTx4MK6urgBMnjyZPn36EBgYSJMmTejSpQuRkZE899xzBa7nbqZNm0bPnj1LtEtQuymXh8ne3l70p7lHLILJw6liH9J5eUylF0yKh8lWcpiUkNxdlb5tQEDYsocptYhVcjUpJAemHKZr166xdu1aIiIi2LZtG+3atWPJkiX3dFzL+2B5XxYtWkRERAR16tQBIDIykvT0dP74449C50dHR7Nz5042bdpU6P41a9Yo3iWA7t27c+7cObZs2UJUVBTHjh2jW7dufPTRR6W212KrWq1m8eLFXLlyhVmzZuHn58f06dNp0aIF8fHxAPj6+rJ3716OHj3KqFGjyM3NZciQITzxxBMF0krupkOHDqWySVC7Kdcd85133uHzzz9n7ty5YmluOckwF5d01lbsQ8vBTk0SuWTmlL4WU14Ok20IpiJDcjbgYTK1RrHNHCbFw6Qp/Huixl7FiM973E+TlPOWFZ1OR8+ePenZsyeTJk3ilVdeYfLkyQwdOrTIOY0aNUKSJI4fP84//vGPAvtPnjyJh4cHderUwWAwsGzZMhISEqyEtcFgYNGiRUrtvfyEhIQwfPhwxo8fXyBBPDc3lw0bNjBhwgSr7XZ2dnTr1o1u3boxfvx4oqOjmTZtGuPGjcO+mGbYBoOBuLg4OnbsaLW9fv36DBo0iEGDBhEdHU2TJk1YsGABU6dOVca0bNmSli1b8sYbb7Br1y66devG9u3bi81LcnJyKnKfwHYo19N6165dbN26lf/7v/+jRYsWBW68hS35FFhjyTFytK94wZT/+KVBzjavktPahmBSClcaTPkjtuVhUttk811Zlkm15DCp1SAbCuyXJKnUobHqRvPmzVm9enWxY7y8vOjZsyfz5s3j7bfftspjSkhIYPny5QwePBhJkpTcosOHD5sWCpg5efIkAwYMIDExES8vrwLnmDRpEiEhIQVKEGzduhV3d3ceeOCBEq9Dr9eTlZVVrGBaunQpd+7cISoqqsgxHh4e+Pr6kp6eXuz5gGLHCAQWynXHdHd359lnn61oW2wKi4eptCvZSkt5ilfamodJyp/Dotfn5TDZQNK3rTbfTTcYsQTeXDRqyDXUSO94YmIiffv25eWXX6Z169a4uLhw4MABZs2axTPPPKOMu3r1KrGxsVZz/f39mTt3Ll27diUiIoLo6GiCgoI4duwYY8eOpX79+kyfPh0wheN69+5dYBFPixYteOutt/j+++8ZPXp0Afvq1avHmDFj+OSTT6y2r1271iocB6YaSy+88AIdOnTAy8uL48ePM3HiRMLDw5W8IzCVNkhISECv13P16lVWrVrFnDlzeO211xSv0MKFC4mNjeXZZ58lJCSErKwsli1bxrFjx/jyyy8BeO211/Dz8+ORRx6hQYMGxMfHEx0djbe3N126dAFMvVEnTJjAyZMny/KxCGyEct0xi1opISg9ln5vjnYVLJjsyp7DZFRWydmGYLKE5MAUljPk2k5IztQaxfYqfVvCcWoJHFQSd68vy58PU51xdnbmwQcfZM6cOZw9e5bc3FwaNmzI8OHDmThxojLu008/5dNPP7Wau3jxYoYOHcqBAweYMmUKzz//PImJifj4+PCPf/yDyZMn4+npyfXr1/n999/573//W+D8kiTRp08fFi1aVKhgAhg7dizz588nK1+PyrVr1/Ltt99ajYuIiGDp0qVMnDiRjIwM/Pz8ePLJJ5k0aZLVuK+//pqvv/4ae3t7vLy8aN++PStWrLD60t6pUyd27drFyJEjuXbtGs7OzrRo0YLVq1cr5Rcee+wxvv32W+bPn09iYiJ16tShS5cubNmyRfGWJScnc+rUqdJ8FAIb5J6+Yt68eZNTp04hSRJNmjTB29u7ouyq9Vg8QNXBwySbPUwqR9taJQcg6w02FpLTKHWYbMnDlGKp8q1WFyqMakrit1arZcaMGcyYMaPIMXdX176bgICAYr/01qtXj1yz17UwvvjiC+X/U6ZMYcqUKVb7XVxcuHnzpvLzoUOHSElJUYSLhQkTJhTIabqbbdu2FbvfQtu2bfnuu++KHRMVFVVsCA9g6NChVnlgYWFhNeZ3Q1D5lGuVXHp6Oi+//DK+vr50796dbt264efnx7Bhw8jIyKhoG2sllRWS0yk5TGXxMOX1krMFrENyuTZVuFKlVttkSC7NvELOOV8NpprgUaoN6PV6peq2QFCTKZdgGjNmDNu3b2fdunUkJSWRlJTEmjVr2L59O++8805F21grUUJyFSyYLMfLLItgyjSJXJWDY4XaUm3JJ5hkgyFf4craLyDUGjsl6duWHmB5NZgKv+UJL0Ll0alTJwYNGlTVZggE90y5nhArV67kl19+ISwsTNkWGRmJg4MD/fr1syqtLygcyyo2hwruX1aeHCYlJGcrSd+SBBoN6PWmkJytFa60QQ9TShGNdwUCgaC0lMvDlJGRQb169Qpsr1u3rgjJlZLMXNMNvKI9TJaQXNkqfVtCcraRwwT5i1fmC8nZgGBSqzUg2V4OU2EhufwID5NAICiJct0xu3TpwuTJk1m2bBk6c95LZmYmU6dOVZZnCoonU6nDVDlJ32XJYZIzzavkbMTDBCbBJFO9QnL6HAOJV9NJT8rGaJRNndFzDORmG9HYqfD0c6JeoCvSPTRrVqnzWqPYYkjubg+TyGMSCASlpVxPiM8//5wnnniCBg0a0KZNGyRJIjY2Fp1Ox8aNGyvaxlqJRdDoKrisgGO5ygrYVtI3AGaxYBWSq6Kk79TbWez/7Tyn9iVg1Bfv6XCv58hjQ5tTL8i12HFFodLYZmuUFIuHSeQwCQSCclKuO2bLli2Ji4vj+++/5+TJk8iyTP/+/RkwYECBLtiCwqmspG+Lhyk9uyxJ35Zecrbz2VlCcoacbGRzHynVfRYQsixzfNc1dv9yhlzz5+XgYodrHQfU5vYdGnsVdlo1udlG4s8mkXQ9g1WfHKT7C01o0a1+mc8pqVQ2Wek7zVLlW3iYBAJBOSn3HdPBwYHhw4dXpC02gyzLZORaBFPFPrRcdKbjpWWXoTWKRTDZSB0myBNMxpwcZZvmPoaocrMNbP3uBHEHbgDgE+xG16hG+AS7FvkQz87IZet3Jzl7+Cbblp9Cn2OkzaMNy3bifF3ZbUkwKSE5tchhEggE5aPcd8xTp07x5ZdfcuLECSRJolmzZvzzn/+kWbNmFWlfrSTXIGMwmm7QFV2HyUVneuinZBZdeO5ubDMkZ/rV12fn1Xy+X0nfaXey+X3eEW5dTkOlknjwH8G0fcy/xNwkraMdESNasm/deQ6sv8Cun+OoG+CCbyP3Up9bzrfOw5ZymJSQ3F2Nd4WHSSAQlJZyrZL75ZdfaNmyJQcPHqRNmza0bt2aQ4cO0apVK37++eeKtrHWkb9GUkWH5FzNgik1q/QeJotgUtlQOFVSWwRTXvsGlbryPS7xZ5L4ecZ+bl1Ow8HFjmfebku7xwNKncgtSRKdngqiUfu6AJyNvVnCjCKQZaumqrWdtCLKClgEk/AwCe4XgYGBfPbZZ1VqQ0JCAj179sTJyQl3d3fA9LdgaeB84cIFJTdZkEe5BNN7773HhAkT2Lt3L7Nnz2b27Nns2bOHiRMnMm7cuIq2sdaRYe5dZqeWsCsiCbW8WEJyqVml8zDJspwXkrMhD9PdITm1RlOp3gZ9joF9v53n19mHyUjJwdPPiefGdcCvsXuZjyVJEsFtTW2Irpy8U6a5RvMlStiWQEhRClfW/JDcjRs3ePXVV/H390er1eLj40NERAR79+4FSn4gX758mWHDhuHn54e9vT0BAQGMHj2axMTEQsePGDECtVrNjz/+WGDflClTkCSJkSNHWm2PjY1FkqQCbVouXryIVqslJSWF9PR0xo0bR3BwMDqdDm9vb8LCwvjtt9+U8WFhYUiShCRJaLVa6tevz1NPPcWqVasK2LJ161bCw8Px9PTE0dGRxo0bM2TIEPT6vC+PCxcupE2bNopQaNu2LTNnzizyvaoM9u/fz4gRI+7rOe9mzpw5xMfHExsby+nTpwGIj4+nV69ehY7ftm0bkiSRlJR0H62sfpTraZ2QkMDgwYMLbB84cCAJCQn3bFRtp7JWyEF+wVQ6D5OckwPmh4VkSx4mS0jOLJhUlbBCLvFqGke2XGbTomMsmbCb/b+dRzbKNO5Yj6j32uNap/zvd/0mHqZzXEkjMzWnhNF5WGSBVIMEQkWQWotCclFRURw5coSlS5dy+vRp1q5dS1hYGLdv3y5x7rlz5+jQoQOnT5/mhx9+4MyZMyxYsIAtW7bQpUuXAsfIyMhgxYoVjB07lkWLFhV6TJ1Ox6JFi5QHb3GsWbOGsLAwXF1dGTlyJKtXr2bu3LmcPHmSDRs2EBUVVUC4DR8+nPj4eM6cOcPKlStp3rw5/fv3txIdx44do1evXnTs2JEdO3Zw9OhRpR2L0byoY9GiRYwZM4ZRo0Zx5MgRdu/ezXvvvUdaWlqJdluQZdlKgJWFHPO9xtvbG0fHqu2qcPbsWdq3b0/jxo2pW9fkrfbx8UGr1Vbqee/l/asOlEswhYWFsXPnzgLbd+3aRbdu3e7ZqNpOZa2QA3B1MIfksvVKnlRxGPMVGrUlDxPm8JvB4mGqwHye2/HprPr0ID9+tI9dP8cRt/862el6XDx19BzWnJ4vN8ded2/hP0dXe7zqOwFw9XRSqecZZZNAsDXBlGYovtK3LJvqXuVmZd33V1m8W0lJSezatYuZM2cSHh5OQEAAnTp1YsKECfTu3bvE+W+88Qb29vZs2rSJHj164O/vT69evfjjjz+4evUq77//vtX4n3/+mebNmzNhwgR2795daGPfpk2bEh4ezgcffFDi+desWcPTTz8NwLp165g4cSKRkZEEBgbSvn173nzzTYYMGWI1x9HRER8fHxo2bEjnzp2ZOXMmCxcu5Ouvv+aPP/4AYPPmzfj6+jJr1ixatmxJSEgITzzxBN988w329vbK+fr168ewYcNo1KgRLVq04IUXXuCjjz4q0l6LZ2Xjxo106NABrVbLzp07mTJlCg888AALFy6kYcOGODo60rdvXysPzNChQ/nHP/7BjBkz8PPzo0mTJkBBD6AkSSxcuJAnn3wSR0dHQkND2bt3L2fOnCEsLAwnJye6dOnC2bNnrWxbt24d7du3R6fTERwczNSpU0slRgIDA1m5ciXLli1DkiSl2XD+kFx+Lly4QHh4OAAeHh5Wc2RZZtasWQQHB+Pg4ECbNm345ZdfSnz/airlums//fTTjBs3joMHD9K5c2cAYmJi+Pnnn5k6dSpr1661GiuwJrOSVshBnocJTCvl3ByKFwKyJeHbzg7JhlZNKWUFck1J3xVVtPLi34ls+Ooo+hwjKrVEg2ae+Ia44tvIHd8QN1QVGIKtG+hK4tV0bsenl3qO8mi2McFkSfouKiQHpgUAXwx57n6ZpDBq6S/YlfLLirOzM87OzqxevZrOnTuXySNw+/ZtNm7cyPTp0wuUf/Hx8WHAgAGsWLGCefPmKZ63RYsWMXDgQNzc3IiMjGTx4sVMnTq1wLE//vhjOnbsyP79++nYsWOh509KSmLnzp0sWbJEOef69evp06cPLi4upb4OgCFDhvDOO++watUqHnvsMXx8fIiPj2fHjh1079690Dk+Pj5s376dixcvEhAQUKbzvffee3z66acEBwfj7u7O9u3bOXPmDD/99BPr1q0jJSWFYcOG8cYbb7B8+XJl3pYtW3B1dWXz5s3FCuOPPvpISW8ZN24cL774IsHBwUyYMAF/f39efvll/vnPf/J///d/AGzcuJGBAwfyxRdf0K1bN86ePat43CZPnlzstezfv5/Bgwfj6urK559/XmIpoIYNG7Jy5UqioqI4deoUrq6uypwPPviAVatWMX/+fBo3bsyOHTsYOHAg3t7e9OjRo8j3r6ZSrqfE66+/DsC8efOYN29eofvApFgNhtLXA7IVLCE5h0oIyWk1auw1KnL0RlKzcksUTJYaTLYUjgMUcWjIqbiilRePJbJ+wV8Y9TL1m3rw2NBQnD0qz2vn4mk6dtqdrBJG5mE0/yvJxmLH1SYMsky6ofA6TBZqSg6TRqNhyZIlDB8+nAULFtCuXTt69OhB//79ad26dbFz4+LikGWZ0NDQQveHhoZy584dbt68Sd26dYmLiyMmJkbJFxo4cCCjRo1i8uTJqFTWwr9du3b069eP8ePHs2XLlkKPv379elq1akXDhqZSGF999RUDBgzAy8uLNm3a8PDDD/Pcc8/x0EMPlfg+qFQqmjRponi8+vbty8aNG+nRowc+Pj507tyZRx99VBEGYBISffr0ITAwkCZNmtClSxciIyN57rnnClzP3UybNo2ePXtabcvKymLp0qU0aNAAgC+//JLevXvz73//Gx8fHwCcnJysvFxF8dJLL9GvXz8Axo0bR5cuXfjwww+JiIgAYPTo0bz00kvK+OnTpzN+/HjFGxccHMxHH33Ee++9V6Jg8vb2RqvV4uDgoNhZHGq1Gk9PT8DU/swieNLT05k9ezZ//vmn0uEjODiYXbt2sXDhQivBVNj7VxMpl2CyxIQF5aOy2qJYcNVpuJWWU6o8JqM5fq9ydqoUW6otGuscpnsNyV2/kMKGhUcx6mVC2nrT85UWqCs4of9uLGIs7XbpBZPBIpRs6G/YIpYAXArJYbKIJY1Wy6ilv3C/0ZQxbyQqKorevXuzc+dO9u7dy4YNG5g1axbffPONEiopD5b3Ib93KSIigjp16gCmBuvDhg3jjz/+4PHHHy8wPzo6mtDQUDZt2qTkxeQnfzgOoHv37pw7d46YmBh2797Nn3/+yeeff87UqVP58MMPS2WvxVa1Ws3ixYuJjo7mzz//JCYmhunTpzNz5kz27duHr68vvr6+7N27l7///pvt27ezZ88ehgwZwjfffMOGDRuKFU0dOnQosM3f318RS2BqGWY0Gjl16pQiRFq1alWiWAKsxK6lT2urVq2stmVlZZGSkoKrqysHDx5k//79TJ8+XRljMBjIysoiIyPjvuRIHT9+nKysrAJCKCcnh7Zt21ptK+z9q4lU2B3d1rPny4IlJFfRNZgsuJahFpNFMKmdnCvFluqKskouN2+VXHlJvpnJ7/85gj7HSMPmnvQcVvliCcDZ0/SgTbuTXcLIPBTHkg0JJks4zl6S0BbxULQ8fO10uvv+Kk/iuU6no2fPnkyaNIk9e/YwdOjQEj0LjRo1QpIkjh8/Xuj+kydP4uHhQZ06dTAYDCxbtozff/8djUaDRqPB0dGR27dvF5n8HRISwvDhwxk/fnwBj11ubi4bNmzgmWeesdpuZ2dHt27dGD9+PJs2bWLatGl89NFHSoJ0URgMBuLi4ggKCrLaXr9+fQYNGsR//vMf5YG+YMECqzEtW7ZUQmebN29m8+bNbN++vdjzOTmV/IXS8jnm/zxLMw+sa6JZ5he2zeKsMBqNTJ06ldjYWOV19OhR4uLilP6ulY3Flt9//93KjuPHj1vlMfH/7J13fBR1+sffsz29ElIowdAJIFVAEVAxiqcoCKgIeCKKDZD7IaB3ioqncioWDkHlEMspeiCicihwNCkiVQSR3kJCSW+72TK/P2ZnkiWbBtlN4fvmlRfJ7Hd2vjtb5rOf5/k+D1U/D3WdS7pKvPbaayQmJjJixAhAsUOXLFlCXFwcK1asoHPnzjU6yYaGL0NyUL2Vck7VYapmDkF9Rw3JOS6zj5w13853c/ZQlGcnumkwtzycrLU18TUhbocpL8vm8W27IkocpisnVK6ukPMWjquPq+S80b59e68Ju6WJiopi4MCBzJ07l6eeesojdyU9PZ3PPvuM0aNHI0kSK1asIC8vj127dnnU6zpw4AAjR44kIyODqKioMsd47rnnSEpKKlOCYO3atYSHh3P11VdX+jgcDgdWq7VCZ2bRokVkZWUxdOjQcsdEREQQFxdHQUH5OX7t27cHqHBMeZw8eZIzZ84QHx8PwJYtW7RQoa/p2rUrf/zxBy1btvT5sQDtuSidYtO+fXvMZjMnT570CL81ZC5JMM2fP59PP/0UUFYnrF69mpUrV/Lll18yZcoUfvzxxxqdZEPDl6vkoGSlXG4VajG58pUPiisvJKcmfauCqfpvBafDxX/n7yX7bCHBEWb+9Hjny179Vh2CIxSHyWFzYit0YAmqXPS51G/+V5Bgytfyl8oK2fpWuDIjI4Nhw4bx4IMP0qlTJ0JCQti+fTuzZs3ycG9SU1PLFB1s1qwZc+bMoU+fPqSkpDBz5kxatGjBvn37mDJlCgkJCVqIZ8GCBdx2221lvvx26NCBSZMm8emnnzJx4sQy82vcuDGTJ0/mH//4h8f25cuXl1kA1L9/f+699166d+9OVFQU+/fv55lnnmHAgAFa3hEopQ3S09NxOBykpqaydOlSZs+ezaOPPqqt3po/fz67d+/mrrvuIikpCavVyscff8y+fft49913AXj00UeJj4/nhhtuoEmTJqSlpTFz5kwaNWqk5eB8/fXXTJ8+nQMHDlT6XFgsFsaMGcPrr79Obm4uEyZMYPjw4VXKC7pcnnvuOf70pz/RtGlThg0bhk6n49dff2Xv3r3MnDmzxo/XvHlzJEniu+++Y9CgQQQEBBASEsL//d//8dRTT+FyubjuuuvIzc1l8+bNBAcHl1nt2BC4pK/CaWlpWuLed999x/Dhw7n55pt5+umn+eWXX2p0gg0RzWHywSo5KBFM2YXVCMkFX2khOXfSt8MtmC4hh2njl4c4cygbo0XPn57oTFC4b2uYXIzBpMcSrMy7qmE5rdSEy4XrChFNakiuvD5y9Yng4GCuueYaZs+ezfXXX09ycjJ/+9vfGDduHHPmzNHGvf7663Tp0sXjZ/ny5bRq1Yrt27eTlJTEiBEjSEpK4uGHH2bAgAFs2bKFyMhIzp49y/fff+/VvZEkiSFDhpQblgOYMmUKwRd9nixfvrxMOC4lJYVFixZx8803065dO5588klSUlL48ssvPcZ98MEHxMXFkZSUxF133cX+/fu11XwqPXv2JD8/n/Hjx9OhQwf69evH1q1bWbZsmeZ+3HTTTWzdupVhw4bRunVrhg4disViYc2aNZpblpOTwx9//FGl56Jly5YMGTKEQYMGcfPNN5OcnFxmEZSvSElJ4bvvvmPVqlX06NGDXr168eabb1Z79V9VSUhI4IUXXmDatGk0btyYJ554AlBW9z333HO88sortGvXjpSUFL799tsyodKGgiRfwler+Ph4/vOf/9CnTx/atGnDzJkzGTZsGH/88Qc9evQgNzfXF3OtEXJzcwkLCyMnJ8fjW4w/efW/B5i3/ghjr2vB3/7Uvsbv/9mv9/LZzyeZeGMrnhpYsT184b33OP/2O4QPG0bcSy/W+FzqKqefnEDeqlXk/fl+Nu7cQuLV3Rg6vexy6fI4vOMcP3zwG0hw22OdSOwY7cPZls/il7dx4VS+ModOlc9h7f/+x/oNGzBkX2DarDcxVCEhtb6z7GwW4/efoE94MEu7KCEMq9XKsWPHiI6Oxm63ExwcXGufBw2dnTt3csMNN3D+/PkG079wxowZLFu2TLQO8QHqe7NFixZl8rFq+/p9SRbHkCFDuO+++2jVqhUZGRlaOfXdu3f7LaZan/H1Kjm1lEBOFZK+tRymK8xh0kJy7kJv1QnJFeUVs+7fimXfNaV5rYklUFbKXTiVX+XSAk63qyS5XLicDqDhC6Y8tS1KBbll9SUkVx9xOBxa1W2BoD5zSYJp9uzZJCYmcurUKWbNmqXZr2lpaR51mATeKQnJ+VYwVW2V3JWZw1QmJFeNpO/NSw9jK3AQlRBMz9tr13pW85jys6sYklOX2MuuK6ZGWp678a63opUNJem7LtOzZ0969uxZ29O4Yvjss8945JFHvN7WvHlz9u3b5+cZNRwuSTAZjUb+7//+r8z2SZMmXe58rggK1UrfPlolVx2HyZWXB4D+Cl0l56qmw5SRms+BrUq/xP4j2/ilfEBFBLhzmKz5VWu2rLZOkGSX9tgbOhWtklMRDpOgOsyYMYMZM2bU9jS8cscdd3DNNdd4vU24fJfHJWcdf/LJJ8yfP5+jR4+yZcsWmjdvzltvvUWLFi3KJPcJPClZJeebpO9qheQK3CG5K6wOU0lITnkuqpr0/cv3x0CGpK6NiL0qzGfTqyqWYCWkZi2ommDSXCWXrIUjGzpa0ncDLisgEKiEhIRUu9WMoGpc0tfj9957j8mTJ3PrrbeSnZ2tfQiHh4d7NBUUeKfQncPk65BclRwmLSR3ZQkmLSTnrHpZgdwLRRzZeR6AHrfVjVUg1XWY7O4yCpLswnWFhORy1RymCtxA4TAJBILKuCTB9O677/LBBx/w7LPPehQ16969O3v37q2xyTVUfF2HKSzQXVagGiG5Ky6HSe0l576YViWHaf+mMwA0bRdBVELdEJhq7aWqOkxaN3NZTfpu+ORV4DAJBAJBVbkkwXTs2LEyvWIAzGbzJVVMvdLwV9J31VbJXak5TNULyblcMgc2pwHQ7tp4306uGqh1mIqqm8Pk8p7D5MyxIdsbVtuUXHfSd0UhOeEwCQSCyrgkwdSiRQuv9Sf++9//ltsJW1BCoZ9ymIodLqz2isMurjy1rMCVJZhwh+RUl6WykNzZozkU5BRjDjRwVedGPp9eVbGUCslV5aKvhuSQ5TKr5Gwnc0l77RcyPvXea6y+IpK+BQJBTXBJV+wpU6bw+OOPY7VakWWZbdu28fnnn/P3v/+9wgqwAgUth8lHq+SCzQb0OgmnSyanyI6lnOPILldJpe+QuhFi8hdaSM5VtZDc8b0ZADTrEIXeWLsr40qjCiaXU8Zuc1bamkULybnKhuTy1p0Gl4z1jyxsJ3MxN2sYhRxF0rdAIKgJLkkw/fnPf8bhcPD0009TWFjIfffdR0JCAu+++y59+/at6Tk2OAp9nMMkSRKhFgNZhXZyiuw0DvXevdpVWAjub9ZXXvNd5dyric+6Shym43svAJDYqWzD0drEaNKjN+pw2l1Y8+1VFkyS7PJYJee4UIT19wzt7/yNqZhHNgzBpBaurCiHSThMAm+sW7eOAQMGkJWVRXh4eG1PR1DLXPJX5XHjxnHixAnOnTtHeno627ZtY9euXaLSdyUUO1zY3DkVwWbfNWqtSh6TmvCN0Yhk9m8ftFrnoqRvQwU5THmZVjLPFCDpJJq1r1uCCUqtlKtC4rdH0rejJCRX9EcmyKAPU8oU2I5k1/g8awNZlsnRHKbym+/WJ86dO8cjjzxCs2bNMJvNxMbGkpKSwpYtWwBITEyscLXyqVOnGDt2LPHx8ZhMJpo3b87EiRPJyMjwOv7hhx9Gr9fzxRdflLltxowZSJLE+PHjPbbv3r0bSZI4fvy4x/YTJ05gNpvJzc2loKCAqVOnctVVV2GxWGjUqBH9+/fnu+++08b3798fSZKQJAmz2UxCQgK33347S5cuLTOXtWvXMmDAACIjIwkMDKRVq1aMGTOm5DWP0qS3c+fOBAUFER4eTpcuXXjttdfKPVd9+vQhLS2NsLDaLyEiqH2qJZiys7MZOXIkjRo1Ij4+nnfeeYfIyEj++c9/0rJlS7Zu3cq//vWvak1g7ty5Ws+Ybt26sXHjxirtt2nTJgwGA1dffXW1jlfblBYwapNcXxAWqFz4KmrA61SLVgYH18sLx+UguUNwTpciXitK+j5zKBuARk2DtVVpdYnqJH5rZQVcskdIzp6mLNYI6KjkZ7kKHbiq0Ly5rlPocuF0m0cVNd+tTw7T0KFD2bNnD4sWLeLgwYMsX76c/v37k5mZWem+R48epXv37hw8eJDPP/+cw4cPM2/ePNasWUPv3r3L3EdhYSGLFy9mypQp5aZbWCwWFixYwMGDBys9/jfffEP//v0JDQ1l/PjxLFu2jDlz5nDgwAFWrlzJ0KFDywi3cePGkZaWxuHDh1myZAnt27fnnnvu4eGHH9bG7Nu3j1tvvZUePXqwYcMG9u7dq7Vjcbnf4wsWLGDy5MlMmDCBPXv2sGnTJp5++mny3WkJ3jCZTMTGxl5xn48C71TL4njmmWfYsGEDY8aMYeXKlTz11FOsXLkSq9XKihUrtK7QVWXx4sVMmjSJuXPncu211zJ//nxuvfVW9u/fT7NmzcrdLycnh9GjR3PjjTdy9uzZah2ztlEFU6hFyTPyFVVymNQ+cldYOA5Acl88Xa7KQ3LpR3MAiEsK9/m8LgWttEAVBJNnWYESh8merggmU/MQdL+acOUW48iwYgqsewKxOqhtUfQSBHqpw1TfLoTZ2dn89NNPrFu3Tvu8bd68eZVbjzz++OOYTCZ+/PFHAgICAGjWrBldunQhKSmJZ599lvfee08b/9VXX9G+fXumT59OXFwcx48fJzEx0eM+27RpQ0xMDH/961/58ssvKzz+N998w5AhQwD49ttvefvttxk0aBCgOGPdunUrs09gYCCxsbEANG3alF69etG2bVsefPBBhg8fzk033cSqVauIi4tj1qxZ2n5JSUnccsst2t/ffvstw4cPZ+zYsdq2Dh06VDhfEZITlKZaDtP333/PwoULef3111m+fDmyLNO6dWv+97//VVssAbz55puMHTuWhx56iHbt2vHWW2/RtGlTjzesNx555BHuu+8+evfuXe1j1jaqgAnz8YWoOiE5/RVWtBJAMqpJ38oF1VBB0nfaEUUwxSbVTVveUo3ilaXLCjjdDpPslLGnFwJgjAvGEKXkvDkyinwxXb+iJnyH6PUViiNZlpFlGVex0+8/1XG3goODCQ4OZtmyZdhsVesfqJKZmckPP/zAY489poklldjYWEaOHMnixYs95rNgwQLuv/9+wsLCGDRoEAsXLvR636+++ipLlizhl19+Kff42dnZbNy4kTvuuEM75ooVK8hTUwOqwZgxY4iIiNBCc7GxsaSlpbFhw4Zy94mNjWXr1q2cOHGi2scTCKCaDtOZM2do3749gBZ3fuihhy7pwMXFxezYsYNp06Z5bL/55pvZvHlzufstXLiQI0eO8OmnnzJz5sxLOnZtklNUDJQIGl8RFmBwH6+CkFyuu2jlFegwqTlMql2vK0cwFRc5yExVnLi4lnVUMFWjeGVJWYGSHCZHRhE4XEgmHYZIC4aoAIqP5eK4UP8FU2UlBUqLKNnu4sxz5X/2+Ir4F/sgVXEBiMFg4KOPPmLcuHHMmzePrl270q9fP+655x46depU4b6HDh1CluVyS7+0a9eOrKwszp8/T0xMDIcOHWLr1q2aKLn//vuZMGECzz//PDqd53ftrl27Mnz4cKZNm8aaNWu83v+KFSvo2LEjTZs2BeD9999n5MiRREVF0blzZ6677jruvvturr322krPg06no3Xr1lqO1LBhw/jhhx/o168fsbGx9OrVixtvvJHRo0cTGqosXnj++ecZMmQIiYmJtG7dmt69ezNo0CDuvvvuMo9HIPBGtV4lLpfLo3mfXq8nKOjSKkRfuHABp9NJ48aNPbY3btyY9PR0r/scOnSIadOm8dlnn2GoYrNUm81Gbm6ux09togqY8ACTT4+jCrLcCkNyqmC6Ah0mNelbrjiH6dyJXGQZQqIsBIXVzcT4qobkXC6XJhCRZVwOZbw9TRGExtggJJ2EIVpxHxwZVh/N2H/kVpDwXZr6lsN05swZli9fTkpKCuvWraNr16589NFHl3W/6jlQReSCBQtISUkhOjoagEGDBlFQUMDq1au97j9z5kw2btzIjz/+6PX2b775RnOXAK6//nqOHj3KmjVrGDp0KPv27aNv37689NJLVZ6vOle9Xs/ChQs5ffo0s2bNIj4+npdffpkOHTqQlqYUnI2Li2PLli3s3buXCRMmYLfbGTNmDLfcckvJ+0IgqIBqOUyyLPPAAw9gdq+oslqtjB8/voxo8raCoTwutslLvwlK43Q6ue+++3jhhRdo3bp1le//lVde4YUXXqjyeF+jJmH73mGqPCTndBet1F9pRSspSfp2uZSLRHkCPCNVye2JblJ3RaU5UJm7rajiVielVwspITlFTNjPKU6SISZQ+b8hheScJSG5ypCMOuJf7OPrKXk9bnWxWCwMHDiQgQMH8txzz/HQQw/x/PPP88ADD5S7T8uWLZEkif3793PnnXeWuf3AgQNEREQQHR2N0+nk448/Jj093eO94XQ6WbBgATfffHOZ/ZOSkhg3bhzTpk0rkyBut9tZuXIl06dP99huNBrp27cvffv2Zdq0acycOZMXX3yRqVOnYjKV/6XS6XRy6NAhevTo4bE9ISGBUaNGMWrUKGbOnEnr1q2ZN2+exzUgOTmZ5ORkHn/8cX766Sf69u3L+vXrGTBgQLnHEwigmoJpzJgxHn/ff//9l3zg6Oho9Hp9GTfp3LlzZVwngLy8PLZv386uXbt44oknAOUbsyzLGAwGfvzxR2644YYy+02fPp3Jkydrf+fm5mqWcG3grxwm1cHKLiwud4yW9H0F5zC55IpDchnucFxUPRBMxZUIJi0cBx695JyZipNkiArw+N9xoQhZlik+lYczy0pAp0b1Lkk6r4K2KODZGkWSpCqHxuoa7du3Z9myZRWOiYqKYuDAgcydO5ennnrKI48pPT2dzz77jNGjRyNJkpZbtGvXLo9+oQcOHGDkyJFkZGQQFVW2xMZzzz1HUlJSmRIEa9euJTw8vNJVze3bt8fhcGC1WisUTIsWLSIrK4uhQ4eWOyYiIoK4uLgK23WpKSaipZegKlRLMJWX8HcpmEwmunXrxqpVq7jrrru07atWrWLw4MFlxoeGhpZp7Dt37lz+97//8Z///IcWLbx3jzebzZojVhfQBJOPHaZgdwHDAlv5rVFcVsVB0AUG+nQudZGSkJziMJUXklMFU3QdabbrDZM7X81WWDWHSXL/aDlMmmBSnCVjowCQlNICzmwbF/61D9nqIGBfBpEj2oLLhe1ELqamoejMdVtgVFTluzT1JSSXkZHBsGHDePDBB+nUqRMhISFs376dWbNmeXxupqamlmlf1axZM+bMmUOfPn1ISUlh5syZtGjRgn379jFlyhQSEhJ4+eWXASUcd9ttt9G5c2eP++jQoQOTJk3i008/ZeLEiWXm17hxYyZPnsw//vEPj+3Lly/3CMeBUmPp3nvvpXv37kRFRbF//36eeeYZBgwYoOUdgVLaID09HYfDQWpqKkuXLmX27Nk8+uijmis0f/58du/ezV133UVSUhJWq5WPP/6Yffv28e677wLw6KOPEh8fzw033ECTJk1IS0tj5syZNGrUSFtA9PXXXzN9+nQOHDhQnadFcIXgu8qJVWDy5MmMGjWK7t2707t3b95//31OnjypFUGbPn06qampfPzxx+h0OpKTkz32j4mJwWKxlNlel8kpVHOYfCuY1Ma+hfbyL6JykXKh1AV4rwTeoFGTvnELJi8hOZdLJuOM8s0zqg4LJnNA9UJyagBIdZjU0JshUnkdSEY9hqgAHBeKyN+YimxVxhX9eoHC1uewHs6iaPd5JLOeqPvbYWkVUdMPqcaoTtJ3fSA4OJhrrrmG2bNnc+TIEex2O02bNmXcuHE888wz2rjXX3+d119/3WPfhQsX8sADD7B9+3ZmzJjBiBEjyMjIIDY2ljvvvJPnn3+eyMhIzp49y/fff8+///3vMseXJIkhQ4awYMECr4IJlNZZ7733HlZrSQ7c8uXLy9ToS0lJYdGiRTzzzDMUFhYSHx/Pn/70J5577jmPcR988AEffPABJpOJqKgounXrxuLFiz2+aPfs2ZOffvqJ8ePHc+bMGYKDg+nQoQPLli3TVnDfdNNN/Otf/+K9994jIyOD6OhoevfuzZo1azS3LCcnhz/++KMqT4XgCqRWBZP6hn3xxRdJS0sjOTmZFStW0Lx5cwDS0tI4efJkbU6xxvGXwxTo7h9XWKHDpHygSZaAcsc0VFSHySXLIHl3mHLOFeK0uzAYdYQ2qrvnyOwO71Y1JKeKBKfDgcvmxOVOFldDcQCGxoGKYNp8xuM+sv5TUpxQtjnJ+s9BGk/uXmedppwG5jCZzWZeeeUVXnnllXLHXFxd+2KaN29eYbSgcePGnuHbi3jnnXe032fMmMGMGTM8bg8JCeH8+fPa3zt37iQ3N7dM6Znp06eXyWm6mHXr1lV4u0qXLl345JNPKhwzdOjQCkN4AA888IBHHlj//v3rzWtD4HtqfS3lY489xvHjx7HZbOzYsYPrr79eu+2jjz6q8A0zY8aMMrZzXcdvgsmkCAK1b503XEVK7Z0r0WHSkr7df3tzmDLd1a8j44PQ+bDI6OViqqbDpHcLJpfTqYXjdIEGdAEl58DY2DNMG/1gMrqgktstbSLQR1pw5hST9fUhZFfdvKhUFpKrbw5TfcThcGhVtwWC+kytC6YrjVyru9K3rwWT+xt/YXFVQnJ11z3xFVrSt/tvvZek7xz36rGwmLqd46UmfTtsTpzO8pdHayE5XYnD5HSH4/SRnqLZGFuy8tUQE4C5ZThR97fH1CIMQ+NAwv50FRFDWoIOinafJ/vrw8jOuieaShrvVv5RJ5wE39CzZ09GjRpV29MQCC6bWg3JXYnkufNBQi2+dpgUwVRkFyE5b2hJ38iA5D0kd94tmOpwOA7AZClxT4qLHAQEe19dpAkmLw6T4WLBVMphCr2xGZJOwtwijJhHSoojGhsFEjGsDVlf/kHBL+m4iuxE3tsWyUsLktqidKVvbwiHSSAQVJW688l2haAWkgwN8K1WDXQ7KHanjL0c10Eucq+SuyJDcgYl3VstfOclJKcJppi6LZh0eh1Gt6NYUR6Tmpeid1c1djkdOLJUweT5GA2NArG0icDSPkpryOuNoC4xRI1sB3qJot8yyFp6+LIeS01TWVmB0giHSSAQVIQQTH7E4XRR4M4p8rXDFFCqnkx5eUwlDtOVJ5gwGHGVche8O0xKjldYo7odkoNSxSsrKC1QEpIrEUzOLKUfmT7Cs/SGpJOI/nMy0aPbI1WSvxWQHE3UqPYgQeGOsxTuPV/heH9SnRwmIZgEAkFFCMHkR9RwHECIxbcOk8mgw+C+0JWXx+TSHKa67aD4Aslo8BRMFzlMDruTfLeYqOshOaha4rcqmAxuweR0lHKYIi5PNAe0jSSkv1IQNnv5EeQKQsH+pLKyAgKBQFBVhGDyI2rCd5BJj8EPeR5aLaZyHCa5HgumAnsBecXV73KuIun1uEo5Jzq9p2DKvWAFGYwWPQEhdX91j1btuwKHSQvJuV97LrsTZ7bbYQq//OKuoTc2Qx9uxpVnJ3+b936Q/kSWZa01SnlJ38JhEggEVUUkffsR1WEK8XE4TiXQpCfP6qCo0pBc/RBMucW5fP775yw7vIzT+acBSAhOYESbEQxtPZRQU2gl91CCZDDgcl8r9UZjmeTf0gnf9SExuDoOk9bqwi4ju+t0GSIuXzBJBh0hA5qS/fVh8taeIqhbY3Q+dlIrotDlQl24F1pBLzlJkoRYEggElSIcJj+iJnz7OhynElRBLSbZ5UK21o9K3y7ZxZd/fMmtS25lzu45mlgCSM1P5c0db5LynxS+O/pd1e+0VA6Tt4TvPPdy+9Co+iEm1WrfFSV9XyyYDMXKPrpgI5KxZkJWQd0aY4gOwJVvJ2fFMVwVFE71NWrCt16CwCo4ukI0CQSCihCCyY+oS/wDzf4RTCUhubIXUdlm037X1eGk79N5p3nwhwd5aetL5BbnkhSWxN+v+zub793M1vu28kKfF2gZ3pJ8ez7TN05ny5ktVbrf0jlM3mowqflLwTXgvPgDcxX6yakhOcNFgkl/mflLpZEMOsJvvwqAgm3pnHt3V62JppxSJQUqcgnrg4MoqB3WrVuHJElkZ2df9n3179+fSZMmXfb9XA6FhYUMHTqU0NBQ7XElJiby1ltvaWMkSaq0kfOVihBMfkQVTJYqFNGrCbRaTF4cJjXhG+ruKrn/Hvsvd397NzvO7iDAEMC0ntP4zx3/4fak2wkxhRBkDGJIqyF8dftX3JJ4CwArj6+s0n1LBoOWw6Tz4jCVCKa6eW4uxhRY/ZCc0a4IRUMN5C+VxtImkvA7k5RjXiiicPe5Gr3/qlLdhO/64jCdO3eORx55hGbNmmE2m4mNjSUlJYUtW5QvCxdfAC/m1KlTjB07lvj4eEwmE82bN2fixIlkZGR4Hf/www+j1+v54osvytw2Y8YMJEnS+n+q7N69G0mSyrRpOXHiBGazmdzcXAoKCpg6dSpXXXUVFouFRo0a0b9/f777rsQp7t+/P5IkIUkSZrOZhIQEbr/9dpYuXVpmLmvXrmXAgAFERkYSGBhIq1atGDNmjPa6B6VJb+fOnQkKCiI8PJwuXbrw2muvlXuu+vTpQ1paGmFhYeWOqSpLly7lpZdeuuz7uRwWLVrExo0b2bx5s/a4fvnlFx5++GGv448fP44kSfWuo4avEILJj1jtSoig9JJ/XxLgDskVeAvJuQWTZDYjVZDfURtYHVZe3PIiT294mgJ7AV1iurDkjiWMbDcSg66suDHoDNzVSmnEueH0Blxy+dWuVZQcJkUwGbw6TEq4sr44TGoOU0VJ39oqObdANDiUApcXlxSoCYJ7xRN2m9tp2pJWK2KkpKRAxR9z9c1hGjp0KHv27GHRokUcPHiQ5cuX079/fzIzMyvd9+jRo3Tv3p2DBw/y+eefc/jwYebNm8eaNWvo3bt3mfsoLCxk8eLFTJkyhQULFni9T4vFwoIFCzh48KDX20vzzTff0L9/f0JDQxk/fjzLli1jzpw5HDhwgJUrVzJ06NAywm3cuHGkpaVx+PBhlixZQvv27bnnnns8LvL79u3j1ltvpUePHmzYsIG9e/dq7VhcLuXzYMGCBUyePJkJEyawZ88eNm3axNNPP01+fn658zWZTMTGxl7Wa0R1diMjIwkJCbnk+6kJjhw5Qrt27UhOTtYeV6NGjQgM9H3plIr6E9YXhGDyIyUOk38EitqAt8hLSE5N+K5r4bjjOce5b8V9fHXwKyQkxnUcx8KUhTQNaVrhft0bdyfQEMiFogv8nvF7pccpLZi85TDV15BcsbUKITn34zU5FcFkCPfNayCoWwzoJezpBVq9J39SWQ2mi3G5XBQXF/v9pzpiMjs7m59++onXXnuNAQMG0Lx5c3r27Mn06dO57bbbKt3/8ccfx2Qy8eOPP9KvXz+aNWvGrbfeyurVq0lNTeXZZ5/1GP/VV1/Rvn17pk+fzqZNm7w29m3Tpg0DBgzgr3/9a6XH/+abb7jjjjsA+Pbbb3nmmWcYNGgQiYmJdOvWjSeffJIxY8Z47BMYGEhsbCxNmzalV69evPbaa8yfP58PPviA1atXA7Bq1Sri4uKYNWsWycnJJCUlccstt/Dhhx9iMpm04w0fPpyxY8fSsmVLOnTowL333luh63NxSO6jjz4iPDycZcuW0bp1aywWCwMHDuTUqVPaPjNmzODqq6/mX//6F1dddRVmsxlZlsuE5BITE5k5cyajR48mODiY5s2b880333D+/HkGDx5McHAwHTt2ZPv27R5z2rx5M9dffz0BAQE0bdqUCRMmUFBQUOm579+/P2+88QYbNmxAkiT69++vzaM8R7JFixaA0ty49D4ACxcupF27dlgsFtq2bcvcuXO121Rn6ssvv6R///5YLBY+/fTTSudY1xGr5PyIzS2Y/OcwKcdRna3SuNx95KQ6UlJAlmWWHV7GK9teochRRKQlklf6vkKf+D5V2t+kN3FN3DWsPbWW7We30yG6Q8U7GEuSvi8OyckumQL3cvugeiKYTJbKBdPFDpPJpQilmigp4A1doBGdxYCrwF4rdZlK+shV/H5T3QO73c4bb7zh83ldzDPPPKNd1CsjODiY4OBgli1bRq9evTCbq/7cZWZm8sMPP/Dyyy8TcNH7PjY2lpEjR7J48WLmzp2rnZMFCxZw//33ExYWxqBBg1i4cCEvvPBCmft+9dVX6dGjB7/88gs9evTwevzs7Gw2btzIRx99pB1zxYoVDBkypNrOy5gxY/jLX/7C0qVLuemmm4iNjSUtLY0NGzZ4NHC/+DGuX7+eEydO0Lx582odrzSFhYW8/PLLLFq0CJPJxGOPPcY999zDpk2btDGHDx/myy+/ZMmSJSWrUr0we/Zs/v73v/O3v/2N2bNnM2rUKK699loefPBB/vGPfzB16lRGjx7Nvn37kCSJvXv3kpKSwksvvcSCBQs4f/48TzzxBE888QQLFy6scN5Lly5l2rRp/PbbbyxdurRKr7lt27bRs2dPVq9eTYcOHbR9PvjgA55//nnmzJlDly5d2LVrF+PGjSMoKMhD8E6dOpU33niDhQsXVuu1WlcRDpMfUXOJLEb/nHb1OFYvFyvZ6q7BVAccpvSCdCasncBzm5+jyFFEj9gefHX7V1UWSyqJYYkAnMk/U+lYD4dJ5/mBVphXjMspgwRBPhITNY3RorZGKV+YqILJ6CfBBIA7T6w2GvPmulfJlddHTkUVB/Uhh8lgMPDRRx+xaNEiwsPDufbaa3nmmWf49ddfK9330KFDyLJMu3btvN7erl07srKyOH/+vDZ+69atjBgxAoD777+fhQsXaiGu0nTt2pXhw4czbdq0co+/YsUKOnbsSNOmilv8/vvvs3nzZqKioujRowdPPfWUh+ioCJ1OR+vWrTXHa9iwYdx7773069ePuLg47rrrLubMmUNubq62z/PPP094eDiJiYm0adOGBx54gC+//NLr46kIu93OnDlz6N27N926dWPRokVs3ryZbdu2aWOKi4v55JNP6NKlC506dSo3pDdo0CAeeeQRWrVqxXPPPUdeXh49evRg2LBhtG7dmqlTp/L7779z9uxZAP7xj39w3333MWnSJFq1akWfPn145513+Pjjj7G6owbloeZ2qWHGyMjISh9ro0ZKW6SoqCiPfV566SXeeOMNhgwZQosWLRgyZAhPPfUU8+fP99h/0qRJ2pj4+PhKj1fXEQ6TH7E6VMHkH4fJ7P5mrR63NGrSd206TMXOYj77/TPe2/MeRY4iDDoDj3V+jAeTHywjYqpCQlACAGcKqiCYdDpc7qXmF38D1NylUJNW5LGuY6pCSE5zmIxG9JIBE4pQqumk79JobVVc/hcjedUMyRkMBp555hlfTskrRi9teSpi6NCh3HbbbWzcuJEtW7awcuVKZs2axYcffsgDDzxwyfNQBWNpdyklJYXo6GhAubiPHTuW1atXc/PNN5fZf+bMmbRr144ff/yRmJiYMreXDscBXH/99Rw9epStW7eyadMm/ve///H222/zwgsv8Le//a1K81XnqtfrWbhwITNnzuR///sfW7du5eWXX+a1115j27ZtxMXFERcXx5YtW/jtt99Yv349mzdvZsyYMXz44YesXLlSaxlUGQaDge7du2t/t23blvDwcH7//Xd69uwJQPPmzTWxURGdOpU0s27cuDEAHTt2LLPt3LlzxMbGsmPHDg4fPsxnn33mcR5cLhfHjh0rVwzXJOfPn9cWDowbN07b7nA4yiTHlz5PDYH6cTVoIBQVK99k/CWY1ON4D8nVbpXvzambGbJ8CG/ueJMiRxGdG3Xmyz99ybhO4y5JLAHEBccBkJafVqXxsttpuVgwqflLQfVkhRyUDsmV7zCpOUxGo4EAvRICkUw6JF82gta73ZtaEExVzWFSL7qSJGEymfz+cykJxWruzHPPPcfmzZt54IEHeP755yvcp2XLlkiSxP79+73efuDAASIiIoiOjsbpdPLxxx/z/fffYzAYMBgMBAYGkpmZWW7yd1JSEuPGjWPatGll3Dq73c7KlSsZPHiwx3aj0Ujfvn2ZNm0aP/74Iy+++CIvvfQSxcXFFT4Wp9PJoUOHtBwblYSEBEaNGsU///lP9u/fj9VqZd68eR5jkpOTefzxx/nss89YtWoVq1atYv369RUe72K8PWeltwUFBVXpfkqLZXV/b9tUF8zlcvHII4+we/du7WfPnj0cOnSIpKSkaj2GS0WdywcffOAxj99++42tW7d6jK3qeagvCIfJj6hOT4DfBJOih4u8huRqJ+k7tziXv//8d74/+j0A0QHRTOw6kTuS7kAnXZ5+jw9SLN+qhOQAZPeFVFeOYKovCd8AJndIzl6FsgJGg4lAg1IVXR9u9ukqsYsdJmd+MbYj2QR0iEbycXmN3AZaVsAb7du3r7R2TlRUFAMHDmTu3Lk89dRTHnlM6enpfPbZZ4wePRpJklixYgV5eXns2rXL4wvFgQMHGDlyJBkZGURFRZU5xnPPPUdSUlKZEgRr164lPDycq6++utLH4XA4sFqtFebYLFq0iKysLIYOHVrumIiICOLi4ipMiG7fvj1AlZKmVRwOB9u3b9fcpD/++IPs7Gzatm1b5fu4VLp27cq+ffto2bKlz48FaM+B01lyDWncuDEJCQkcPXqUkSNH+mUedQUhmPyI1e85TKrD5C0k5/+k74NZB5nwvwmk5qeik3Tc1/Y+Hr/6cYJNwTVy//HBimDKs+eRV5xHiKniRFKXu39cmRymnJKQXH1BDck57C6cTpfXUKImmExGgjTB5GPB7J6GmsOUu+YkBVvSMF+VTqOHO1Ww4+WTV0kfOZX6VFYgIyODYcOG8eCDD9KpUydCQkLYvn07s2bN8nBvUlNTy9TOadasGXPmzKFPnz6kpKQwc+ZMWrRowb59+5gyZQoJCQm8/PLLgBKOu+222+jcubPHfXTo0IFJkybx6aefMnHixDLza9y4MZMnT+Yf//iHx/bly5d7hONAWbV177330r17d6Kioti/fz/PPPMMAwYMIDS0pM1RYWEh6enpOBwOUlNTWbp0KbNnz+bRRx9lwIABgFJfaffu3dx1110kJSVhtVr5+OOP2bdvH++++y4Ajz76KPHx8dxwww00adKEtLQ0Zs6cSaNGjejduzcAX3/9NdOnT+fAgQPlPgdGo5Enn3ySd955B6PRyBNPPEGvXr00AeVLpk6dSq9evXj88ce1JOvff/+dVatWaY+zJomJiSEgIICVK1fSpEkTLBYLYWFhzJgxgwkTJhAaGsqtt96KzWZj+/btZGVlMXny5BqfR11BhOT8iN8dJveFwuYlJOfvpO/d53Yz+r+jSc1PpUlwEz659ROm9pxaY2IJINAYSLg5HKiay+Ryn5+LcxcK85RwQEA9Ekxq0jeAvZywnBaSM5U4TL7MX4KyDpP1YBYAtqM5FGz3bYPe3FKVvqtCfXCYgoODueaaa5g9ezbXX389ycnJ/O1vf2PcuHHMmTNHG/f666/TpUsXj5/ly5fTqlUrtm/fTlJSEiNGjCApKYmHH36YAQMGsGXLFiIjIzl79izff/+9V/dGkiSGDBlSblgOYMqUKQQHe76vly9fXiYcl5KSwqJFi7j55ptp164dTz75JCkpKXz55Zce4z744APi4uJISkrirrvuYv/+/dpqPpWePXuSn5/P+PHj6dChA/369WPr1q0sW7aMfv36AXDTTTexdetWLaF66NChWCwW1qxZo7llOTk5/PHHHxU+B4GBgUydOpX77ruP3r17ExAQ4LWopy/o1KkT69ev59ChQ/Tt25cuXbrwt7/9jbi4OJ8cz2Aw8M477zB//nzi4+O15/Chhx7iww8/5KOPPqJjx47069ePjz76qEyItKEhyfXhU6IGyc3NJSwsjJycHI9vMf7gzwu3sfaP88y6uxPDu1dcV6gm+GLbSaYt3cuNbWNY8IDnUt8L8+Zz/q23CLt7KPEzZ/p0HoeyDjHmv2PIs+fRrXE33h7wNmHmy6+c643h3w7n98zfefeGd+nftH+FY/9760D2h5pp06krf3r2RW3793N/5fivF+g/sg0d+ib4ZJ6+YP6T63DYXYya2ZvQ6LLO4Ztvvklubi5Dbk0h++OdXBXSidCBzQm9sZnP5nT2nZ3YzxQQ/ecOWNpEcv6DX7EdyVFulCBqVHsC2pcN7dQEN/5ygH35Vv7d6SpuiPJ8r1utVo4dO0aLFi0oLCzEarUSFhbW4HIu6gI7d+7khhtu4Pz589VOcK9rfPTRR0yaNKlGWqUIvFP6vWm56At9bV6/QThMfkWr9O3vpG9vq+RUh8nsW4cptziXSWsnkWfPo2tMV9676T2fiSUoCctVyWHSl+Mw5bodppD64zABGAMqTvxWQ3ImkxmLXhEGel+7aDrPpG+Xe+GDLsgAMmT95yCOHN8UtVTLClR1ldwV9t3RbzgcDq3qtkBQnxGCyY9olb79nPTtbZWcbFUuUroA3wkmWZaZsXkGJ/NOEh8Uz9sD3ibA4NucqdigWADOFp6tdKxWVuCiZPMit2AKrEchOShJ/C4uJ/G7dEjOrFeeB12Qby9iF4fkZJsyt8gRbTEmBOMqdJC15JBPxEpVe8nVpxym+kjPnj0ZNWpUbU+jwbNx40atsKm3H8HlI5K+/YjV7t+kb3NFSd9WtZec7wTTfw79h1UnVmGQDLzR/w3CLeE+O5ZKhDkCgGxbdqVjZXeyd+nVebIsazlM9U8wVVyLSXOYzCbMOqV3lC7Yx9/6L3KYZJvbYQo0EHlPG86+vRPbwSyK9pwn8OqytXsuFVmWq530LRwmQWU88MADl1Xrypd0795dNMn1MUIw+RFVuPgv6bt8weRrh+lozlFmbZsFwMSuE0mOTvbJcS4mwqIIpkxr5Y1IXToJkNGVchjsVidONXRa3wRTgNth8iKYnE6nJghMJgtOvSKY9H52mFzulaKSSY+xUSChA5qRu+oE2d8dxdI2Ep2lZj6SCp0u1OLiVQ3JCQT1mYCAAL+VG7hSESE5P6KGxupCSM5lc5cV8IHDZHVY+b/1/4fVaaV3XG9Gdxhd48coj0iLUro/y5pV6ViXTg3JlQgmNX/JaNZj9FPPv5pCc5i8tEcp3SncqDdh0Cli0NchuZLClYqDI9vcgsmsnNuQfk0wRAfgyreT++OJGjtsrttd0ksQWEkFZ+EwCQSCqiAEkx/xfw6Tchybl6RvWa3DZKnZZeWyLPPyzy9zKOsQkZZIXr7u5csuSFkdVIepKoJJdrsfpR2m+lhSQKWikJwajgMwupTXhdPlQPaxx6w5TE5Z+XE7TTq3YJIMOsIHKxWK87ecoTg1v0aOqyV86/VVzlESgkkgEFSEEEx+xN85TBW2RrGplb5rNgn78wOfs+zwMnSSjlf7vkqjwMr7KdUkag5TVQST030h1VFyQdUSvuvZCjkoVe3byyo51WEyGAzoi5XHa3MV4nKUXxm8RigVknPZSuYllfrSYGkVQUDnRsqquaWHkJ3Va4bqjaomfINI+hYIBFVDCCY/4XLJ2Bz+LiughuT84zD9lPoTr/3yGgBPdX2K3vG9a+y+q4rqMOXZ87C77BWOld2hmtJvgsJ6ukIOSpUV8LJKTqvybTSC1V1E0lmIw15xz67LplTStxqOw6BD0nuKlPDbrkIKMGBPzSdv7anLPmxV+8iVRjhMAoGgIoRg8hOqWAI/huTcFwuHS8Zx0bd2l82d9F1DDtOxnGNMWT8Fl+zizpZ3MqbDmBq53+oSZg7TQoDZ1uwKx7rc1+zSb4IiNSQXUv9qxmhlBbyE5Eo7THKR8lqwOQtx2isWlZdL6aRv2Z3wrYbjSqMPNRHhDs3lrjlJ0W8XLuu4JX3kKv+IEw6TQCCoCmKVnJ8o3QDX3zlMAFaHi+BS/cVKmu9evsNUYC9g4tqJ5Nvz6RrTled6PVdrFyGdpCPcHE6mNZNMa2aFIUGXOxRX+tmw5isCor4VrYTSOUzlh+SMRiOuAkUU2pyFOB2+FUylHSZthZwXwQQQ0LkRQcdyKPg5nYx//05w73h0ISYcZwtx5hdjbBRIyA1N0QdX/twIh0kgENQ0wmHyE2pYzKTXodf5R0yYS327vjgs57KqIbnLXyX3wpYXOJZzjJiAGN7o/wZGfe26M1oek63iPCZvDpO1QBEQFl+vHvMBpiqE5AwGA073Y7S5CnH402Fyh+R05aw+lCSJ8DtaEtglBlyQv+kMuSuPU7jrHLZD2eRvPsO5uXtwZFkrPW51+sjVN4fp3LlzPPLIIzRr1gyz2UxsbCwpKSls2bIFgMTERN56661y9z916hRjx44lPj4ek8lE8+bNmThxIhkZGV7HP/zww+j1eq/90mbMmIEkSYwfP95j++7du5EkiePHj3tsP3HiBGazmdzcXAoKCpg6dSpXXXUVFouFRo0a0b9/f7777jttfP/+/ZEkCUmSMJvNJCQkcPvtt7N06dIyc1m7di0DBgwgMjKSwMBAWrVqxZgxYzwWPMyfP5/OnTsTFBREeHg4Xbp04bXXXiv3XAkEpRGCyU+oDpPZTwnfADqdpImmiwVTicN0eYJp5fGV/PfYf9FLel7v/zrRAdGXdX81QVVXyqlnRFcqWlkimOqf+Wp0Ozd2WyUOk9tFs/ohJOcth6k8hwlA0ktEDG9N5Mi2BHZvTEDnRoQObE74XS3RR1pwZlrJ/vZopYfNc1a9LUp9KyswdOhQ9uzZw6JFizh48CDLly+nf//+ZGZWXnvs6NGjdO/enYMHD/L5559z+PBh5s2bx5o1a+jdu3eZ+ygsLGTx4sVMmTKl3Ia7FouFBQsWcPDgwUqP/80339C/f39CQ0MZP348y5YtY86cORw4cICVK1cydOjQMsJt3LhxpKWlcfjwYZYsWUL79u255557ePjhh7Ux+/bt49Zbb6VHjx5s2LCBvXv3au1YXC7ltbBgwQImT57MhAkT2LNnD5s2beLpp58mP79mVmYKGj7176pQT/F30UoVi1GPzeEqs1KuJhymbGs2f9/6dwDGdRpHl5gulz7RGqSqxStLHKaSC2WRW0yY66PDVEFrlNIOk0t1mPyRw6QvKStQUrSy8rpIgR0bEdjRM5xqbhHG2bd2YN2fgfVINpak8HLvo6GG5LKzs/npp59Yt24d/fr1A6B58+b07NmzSvs//vjjmEwmfvzxRwIClPzFZs2a0aVLF5KSknj22Wd57733tPFfffUV7du3Z/r06cTFxXH8+HESExM97rNNmzbExMTw17/+lS+//LLC43/zzTcMGTIEgG+//Za3336bQYMGAYoz1q1btzL7BAYGEhurtDxq2rQpvXr1om3btjz44IMMHz6cm266iVWrVhEXF8esWbO0/ZKSkrjlllu0v7/99luGDx/O2LFjtW0dOnSocL4PPPAAixYtKrN97dq19O/fv8J9BQ0P4TD5CaufazCpeFspJ8tyjThMs3fOJsuWRcvwljzc8eHKd/ATVW2P4i4JhK7UhVJzmHzdMsQHqCG5Sh2mQkU82VxFteIweUv6rgrGmECCeigXzvxNFTdXvpSyArIs43QW+v2nOkJN7Qu2bNkybLbqNS3OzMzkhx9+4LHHHtPEkkpsbCwjR45k8eLFHvNZsGAB999/P2FhYQwaNIiFCxd6ve9XX32VJUuW8Msvv5R7/OzsbDZu3Mgdd9yhHXPFihXk5eVV63EAjBkzhoiICC00FxsbS1paGhs2bCh3n9jYWLZu3cqJE1UvkPr222+Tlpam/UycOJGYmBjatm1b7TkL6j/CYfITJVW+/atRvRWvlO12cH8oXqrD9Ov5X1l6SPmweq73c7Wet1SaqobkXG5nSecqLZgUMRFQDwWTGpLzlvRduqyAy+1AFTutPi8r4G2VnHQZFdSD+8RT8HM61gOZOPOK0ZeTnF/iMFX9/eZ0FbFu/TWXPLdLpX+/vejdrWoqw2Aw8NFHHzFu3DjmzZtH165d6devH/fccw+dOnWqcN9Dh5Qmx+3atfN6e7t27cjKyuL8+fPExMRw6NAhtm7dqomS+++/nwkTJvD888+ju6h6eteuXRk+fDjTpk1jzZo1Xu9/xYoVdOzYkaZNmwLw/vvvM3LkSKKioujcuTPXXXcdd999N9dee22l50Gn09G6dWstR2rYsGH88MMP9OvXj9jYWHr16sWNN97I6NGjCQ0NBeD5559nyJAhJCYm0rp1a3r37s2gQYO4++67yzwelbCwMMLCwgBYunQp8+bNY/Xq1ZrjJbiyEA6Tn6i1kJzWT64kJCcXFWm/68zVXyXnkl28uu1VAAYnDa4zoTiVUJPyAZlry61wXIlgUs6N0+7C4XZB6mXSt3uVnN3qKONalC4r4CpUfi92Wf22Sq504cqKcpgqw9g4CFOzEHDJFO46V+646oTk6lvS99ChQzlz5gzLly8nJSWFdevW0bVrVz766KPLul/1NaOejwULFpCSkkJ0tJKXOGjQIAoKCli9erXX/WfOnMnGjRv58ccfvd7+zTffaO4SwPXXX8/Ro0dZs2YNQ4cOZd++ffTt25eXXnqpyvNV56rX61m4cCGnT59m1qxZxMfH8/LLL9OhQwfS0tIAiIuLY8uWLezdu5cJEyZgt9sZM2YMt9xyi5bnVB67du1i9OjR/POf/+S6666r0vwEDQ/hMPkJVbCY60BIzuVuvIteD8bqC4Pvj37P3gt7CTIGManbpJqYZo0SanYLpuKKBZNTvUC4HSY1HCfpJC28VZ8wunOYZBkcxS7NcYKLcphUh8nPITnctcguNSSnEtglhuKTeRT9nknI9U28jslz95ILrcIqORUJM/377b2suV0KOl31a6FZLBYGDhzIwIEDee6553jooYd4/vnneeCBB8rdp2XLlkiSxP79+7nzzjvL3H7gwAEiIiKIjo7G6XTy8ccfk56ejsFQ8l5wOp0sWLCAm2++ucz+SUlJjBs3jmnTppVJELfb7axcuZLp06d7bDcajfTt25e+ffsybdo0Zs6cyYsvvsjUqVMxmcovH+F0Ojl06BA9evTw2J6QkMCoUaMYNWoUM2fOpHXr1sybN48XXnhBG5OcnExycjKPP/44P/30E3379mX9+vUMGDDA67HS09O54447GDt2rEf+k+DKo/5dFeop/u4jp6IKtNJ1oGSr4jDpzOZqf7suchTx1s63ABjXcVydWBV3MWEmxUKvTDC53IJJ5764qgnfliBDvXMdwB2SkwBZKV5ZWjBpOUw6A2qOe7HL6t+yAsWKYLqckBworVQAik/m4rI5vQowrZdcNRwmSZKqHBqra7Rv355ly5ZVOCYqKoqBAwcyd+5cnnrqKY88pvT0dD777DNGjx6NJElabtGuXbvQlxKdBw4cYOTIkWRkZBAVFVXmGM899xxJSUllShCsXbuW8PBwrr766kofh8PhwGq1ViiYFi1aRFZWFkOHDi13TEREBHFxcRQUFFR4PKDcMVarlcGDB9O2bVvefPPNCucuaPgIweQntKTvauRU1ATe+smpDpMUUP1vtp/u/5RzheeID4rn/vb318wka5iqOkyqYJLc7kt9rsEEygXfaNZjtzqVfnJhJbepgkkvK68/J05cstMPDpPyn+yScdmU83w5ITkAfZQFfYQZZ5YN27EcAtpGlhlTnaRvlfqwSi4jI4Nhw4bx4IMP0qlTJ0JCQti+fTuzZs1i8ODB2rjU1FR2797tsW+zZs2YM2cOffr0ISUlhZkzZ9KiRQv27dvHlClTSEhI4OWXXwaUcNxtt91G586dPe6jQ4cOTJo0iU8//ZSJEyeWmV/jxo2ZPHky//jHPzy2L1++3CMcB0qNpXvvvZfu3bsTFRXF/v37eeaZZxgwYICWdwRKaYP09HQcDgepqaksXbqU2bNn8+ijj2qu0Pz589m9ezd33XUXSUlJWK1WPv74Y/bt28e7774LwKOPPkp8fDw33HADTZo0IS0tjZkzZ9KoUSN691baOH399ddMnz6dAwcOAPDII49w6tQp1qxZw/nz57U5RUZGVijoBA0TkcPkJ7Qcpsv8dl1dLF7qMMlq491q5i9lWbP412//AuDJrk9i1tdcH7qapKo5TE7ZHSJSBVN+/V0hp2LSEr89SwuoITlNMOmUv/3bGsV9vi/zPSBJkuYy2Q6VTeyXZbnB5jAFBwdzzTXXMHv2bK6//nqSk5P529/+xrhx45gzZ4427vXXX6dLly4eP8uXL6dVq1Zs376dpKQkRowYQVJSEg8//DADBgxgy5YtREZGcvbsWb7//nuv7o0kSQwZMqTcmkwAU6ZMITg42GPb8uXLPQQdQEpKCosWLeLmm2+mXbt2PPnkk6SkpJQpTfDBBx8QFxdHUlISd911F/v372fx4sXMnTtXG9OzZ0/y8/MZP348HTp0oF+/fmzdupVly5Zp5Rduuukmtm7dyrBhw2jdujVDhw7FYrGwZs0azS3Lycnhjz/+0O53/fr1pKWl0b59e+Li4rSfzZs3V/ZUCRogwmHyE2ovOUs1vvHWBCUOU6kcpqJLq8H0wd4PyLfn0y6yHYNaDKq5SdYwmmAqzvVIDC2N7HJpjoKugThMoJQWKMgpVhymUmhJ3xcJJp8333XXYZKdMi63iJMsl/8eMDUPpWBbOva0sqGUAqcL1U+tSi85lfrgMJnNZl555RVeeeWVcsdcXF37Ypo3b15ueQBQXCJ7BUL6nXfe0X6fMWMGM2bM8Lg9JCTEw43ZuXMnubm5mnBRmT59epmcpotZt25dhberdOnShU8++aTCMUOHDq0whAdK3aXSeWCVnUvBlYVwmPxEUbGaw+TvkJxyvNLNfzWHqRqCKS0/jS8OKHkJk7pO0hrc1kXCzEosyik7KbB7z01wlmqXgPvioDlM9VgwaaUFLqrFpDlM7mqdLr26MtCPDpNVrcN0+d/TDI2UcLLjQlGZ21R3SS9BYDnLxT3mWI8cpvqIw+HQqm4LBPUZ4TD5idorXFkzDtO8X+dhd9npGduT3vG9a3aSNYzFYMGkM1HsKia3OJdgU3CZMaWX00vuc9MQHCajxXs/OS2HyekWTAa3YPJrWYGac5gMUYpgcuYW4yp2eoT5ckutkKuKGKpvrVHqGz179qxyJXKBoC5Td22CBobVofaSqwOCqbAQAF1QUJXu40TuCb45/A0AT3Z5sl58I68s8bu0syK5w1K2ArUtSv39HqG2R7m42rfmMDmUt7zLoIgDX6+SK11WwKU6TJbLP7/6ICO6QOV+LnaZ8txuanUSvkEIJoFAUDFCMPkJdZWa/wtXqknfpVbJuZfQ6gKrtoT6/V/fxyk76ZvQl6tjrq7xOfqCyhK/VaEguWSwu3OY3C1D6rPDpBavvDjpW3OY3JtlkyIO/BaSc8rIbodJVwMOE5S4TI4MT8FU3T5y9eELgEAgqH2EYPITJSE5/55y82U6TKn5qXx/9HsAHu38qA9m6BtUwZRTnOP1dpfbcdHJMnKx22FyV8A2B9ZfwaQWr7w46Vt1mHR2tzgwKf/7q3Cly+ZEzcSWasBhAjBEe89jKikpUL33mnCYBAJBRQjB5CdqPYfJcWkO00e/fYRTdtInvg8dG3X0zSR9gJr4XZ7DpObueAomRVQ0hJBceQ6TrlgRBbK7hIyvc5hUh0ltx4IOpBr60lAimKwe24XDJBAIfIEQTH6i9prvemmNUkWHKduazbLDywB4MPlB30zQR5QuLeANNSTnIZjUpO967TCp/eS8lxXQu6sIqMUjHcU+LiugCSa1aGXNVVE3RCmLFhyZlxeSK41wmQQCQXkIweQnaqv5boC3kFwVHaavDn6F1WmlbWRbesbWr1UulSV9ayE5V4lgUnOYzIENwWEqJyTnbiMomd31mHztMOk9BVNN5S8B6EIUm8yV7/kY8pzutihV7CMnHCaBQFAVhGDyE7W1Ss6rYKqCw+R0Ofnq4FcAjGo/qt5dVCpL+nZe5DA5ip041QbJ9Tjp2+iucWS3lROSs7nbwQQorwu/Nd8trrkaTCp6t2By5nm6ZDnCYRIIBD5ACCY/oRWu9Helb1PZ5ruuwsodpo2pG0krSCPMHEZKYopvJ+kDKkv6dpTKYXLZi7X8JUknaS5NfcTkFkLFReU4TFZ3e5IA95J8f62SU/+uwXOrCibZ6kQu9fqubh+50l8GhGAS+IN169YhSRLZ2dm1Oo9ly5bRsmVL9Ho9kyZN4qOPPiI8PFy7fcaMGZU2TL6SEILJT9RWDpPqMKmCDcBVULnDtOTgEgDuTLqzzvaMqwg1JJdfnO/1ds9Vcnas6gq5gJrLsakNTF4cJqfTiculvP7UOkxqDSN/OUzanzW0Qg7c4svgXu2XV/I4st3iKdzPX078xblz53jkkUdo1qwZZrOZ2NhYUlJS2LJlCwCJiYm89dZb5e5/6tQpxo4dS3x8PCaTiebNmzNx4kQyMjK8jn/44YfR6/V88cUXZW6bMWMGkiQxfvx4j+27d+9GkqQyrUVOnDiB2WwmNzeXgoICpk6dylVXXYXFYqFRo0b079+f7777Thvfv39/JElCkiTMZjMJCQncfvvtLF26tMxc1q5dy4ABA4iMjCQwMJBWrVoxZswY7csCKE16O3fuTFBQEOHh4XTp0oXXXnut3HPlK/r06UNaWhphYWGVD/YhjzzyCHfffTenTp3ipZdeYsSIERw8eLDc8Q888AB33nmn/yZYxxCCyU/YHLXTfLckJFdqlZwakivHYcq0ZvJT6k8A3NXqLh/P0DeEGEMAyCvO83q7R0jObsdWUP/zl6CkrEDpHKbSFwwDOtCBPkAJO/q80rfedw6TJEnog91hufySsFyO+/GGVTH8Xd8cpqFDh7Jnzx4WLVrEwYMHWb58Of379yczM7PSfY8ePUr37t05ePAgn3/+OYcPH2bevHmsWbOG3r17l7mPwsJCFi9ezJQpU8ptuGuxWFiwYEGFF1qVb775hv79+xMaGsr48eNZtmwZc+bM4cCBA6xcuZKhQ4eWEW7jxo0jLS2Nw4cPs2TJEtq3b88999zDww8/rI3Zt28ft956Kz169GDDhg3s3btXa8eifllYsGABkydPZsKECezZs4dNmzbx9NNPk5/v/UuVr7Db7ZhMJmJjY2v1y1l+fj7nzp0jJSWF+Ph4QkJCCAgIICYmxufHLvb1YhMfIQSTn9AcJj9/6w3wFpJTk77LcZh+OP4DDtlBu8h2JIUn+X6SPiDEpAimclfJqSE5d9K3VoOpHucvgffClaUbqerRoQswoDe6hYaPP7guDsnpzDX7+lfDcq680oJJea2HVeO9Vl9cxezsbH766Sdee+01BgwYQPPmzenZsyfTp0/ntttuq3T/xx9/HJPJxI8//ki/fv1o1qwZt956K6tXryY1NZVnn33WY/xXX31F+/btmT59Ops2bfLajLZNmzYMGDCAv/71r5Ue/5tvvuGOO+4A4Ntvv+WZZ55h0KBBJCYm0q1bN5588knGjBnjsU9gYCCxsbE0bdqUXr168dprrzF//nw++OADVq9eDcCqVauIi4tj1qxZJCcnk5SUxC233MKHH36IyWTSjjd8+HDGjh1Ly5Yt6dChA/feey8vvfRSufN94IEHNIer9I/aFDgxMZGXXnqJ++67j+DgYOLj43n33Xc97kOSJObNm8fgwYMJCgpi5syZZUJyaijsu+++o02bNgQGBnL33XdTUFDAokWLSExMJCIigieffBKns+SzvLi4mKeffpqEhASCgoK45pprqtSweN26dYSEKJ+RN9xwg/aYLg7JlWbGjBksWrSIb775psx5SE1NZcSIEURERBAVFcXgwYM9XiuqM/XKK68QHx9P69atK51jXUQIJj9RVEt1mLyG5LSkb+8O04qjKwD401V/8vHsfEdlgqnEYQIcjpI+cg3EYbJ7cZgMej0SEroAIwa3YCrtPvkEH4bkoGSlXOmQnCqYqhOSkyQJWZYpcDgpcPr3pzquVnBwMMHBwSxbtgybzVbl/QAyMzP54YcfeOyxxwgICPC4LTY2lpEjR7J48WKP+SxYsID777+fsLAwBg0axMKFC73e96uvvsqSJUv45Zdfyj1+dnY2Gzdu1ARTbGwsK1asIC/PuwtcEWPGjCEiIkILzcXGxpKWlsaGDRvK3Sc2NpatW7dy4sSJKh/n7bffJi0tTfuZOHEiMTExtG3bVhvzj3/8g06dOrFz506mT5/OU089xapVqzzu5/nnn2fw4MHs3buXBx/0XqKlsLCQd955hy+++IKVK1eybt06hgwZwooVK1ixYgWffPIJ77//Pv/5z3+0ff785z+zadMmvvjiC3799VeGDRvGLbfcwqFDhyp8XH369OGPP/4AYMmSJaSlpdGnT58K9/m///s/hg8fzi233KKdjz59+lBYWMiAAQMIDg5mw4YN/PTTTwQHB3PLLbd4OElr1qzh999/Z9WqVR5h1/pE/b461BPsThdOl/Ih5P86TCUOkyzLSJJUocN0vvA8u8/vBqiXyd4qatJ3XnGe9rhLUyKYFOfPmqsUP6z3DpM7mdvllHHaXeiNOs1hMuiU23QBBvQmd0jO7l+HqaaqfKvoQ9yPw4vDVJ1VcpIkYZWh25bfa3R+VeHI9R0JqmIJBIPBwEcffcS4ceOYN28eXbt2pV+/ftxzzz106tSpwn0PHTqELMu0a9fO6+3t2rUjKyuL8+fPExMTw6FDh9i6dasmSu6//34mTJjA888/j07n+TnWtWtXhg8fzrRp01izZo3X+1+xYgUdO3akadOmALz//vuMHDmSqKgoOnfuzHXXXcfdd9/NtddeW+l50Ol0tG7dWnMxhg0bxg8//EC/fv2IjY2lV69e3HjjjYwePZrQUOWz4Pnnn2fIkCEkJibSunVrevfuzaBBg7j77rvLPB6VsLAwLc9o6dKlzJs3j9WrVxMbG6uNufbaa5k2bRoArVu3ZtOmTcyePZuBAwdqY+677z4PoXTs2LEyx7Lb7bz33nskJSmu/t13380nn3zC2bNnCQ4Opn379gwYMIC1a9cyYsQIjhw5wueff87p06eJj48HFFGzcuVKFi5cyN///vdyz5/JZNJCb5GRkR6PpzyCg4MJCAjAZrN5jP/000/R6XR8+OGH2ufswoULCQ8PZ926ddx8880ABAUFeTh+9RHhMPmB0kv6/e4wlcqZsjlcyE4ncpFS6M9bDtP/Tv4PgE6NOtE4qLF/JukDVIfJ7rJjc5b9Jq4KBZ1byNrylDH1PoepVMhLDcup3/KMerdgCjRgNCmJ/P4qXKn9WcMrEHXBai0m5XEUOV3Y3M9puLHqz2V9CcmBksN05swZli9fTkpKCuvWraNr16589NFHl3W/qrOknosFCxaQkpJCdHQ0AIMGDaKgoEALg13MzJkz2bhxIz/++KPX20uH4wCuv/56jh49ypo1axg6dCj79u2jb9++FYbILp6vOle9Xs/ChQs5ffo0s2bNIj4+npdffpkOHTqQlpYGQFxcHFu2bGHv3r1MmDABu93OmDFjuOWWW7Q8p/LYtWsXo0eP5p///CfXXXedx229e/cu8/fvv3sK7+7du1f6eAIDAzWxBNC4cWMSExMJDg722Hbu3DkAdu7ciSzLtG7dWnMeg4ODWb9+PUeOHKn0eDXFjh07OHz4MCEhIdocIiMjsVqtHvPo2LFjvRZLIBwmv1A64dpczf5Wl4ul1PGKip0Y5ZLQhTeHac1J5dvhTc1u8v3kfEigMRCdpMMlu8grzsNisHjcbncLBb37ImF1X3Drc+NdAJ1OwmDS4Sh2UWx1EhBCGYdJCjCgdwsmezXDOtXF9w6TZ0hOdZd0QLC+eu81iwT7e7bGbPHvqtDActyNirBYLAwcOJCBAwfy3HPP8dBDD/H888/zwAMPlLtPy5YtkSSJ/fv3e13pdODAASIiIoiOjsbpdPLxxx+Tnp6OwVDynDmdThYsWKC5BqVJSkpi3LhxTJs2rUyCuN1uZ+XKlUyfPt1ju9FopG/fvvTt25dp06Yxc+ZMXnzxRaZOnVrhxdXpdHLo0CF69OjhsT0hIYFRo0YxatQoZs6cSevWrZk3bx4vvPCCNiY5OZnk5GQef/xxfvrpJ/r27cv69esZMGCA12Olp6dzxx13MHbsWMaOHVvunEpzsQAPqkLfTqPR87NHkiSv21Rx53K50Ov17NixA/1FDmVpkeVrXC4X3bp147PPPitzW6NGjbTfq3IO6jq17jDNnTuXFi1aYLFY6NatGxs3bix37NKlSxk4cCCNGjUiNDSU3r1788MPP/hxtpdG6ca7/v4ma9DrMLkvHEV2pxaOQ69HuugDqdBeyPaz2wHo37S/P6dZ4+gkHcFG5UPD20o5zWGSlHOjtkWp7w4TlCR+q6UFNIdJKgnJGcxuh8lm8+3KMJ8nfSsXFDXpO1tdIWfQo6vGe01NYg3QSQTp9X79qYnPhPbt21OgvrfLISoqioEDBzJ37lyKijzbyaSnp/PZZ58xYsQIJEnScot27drF7t27tZ+vvvqKZcuWlVuC4LnnnuPgwYNlShCsXbuW8PDwSmv6tG/fHofDgdVqrXDcokWLyMrKYujQoeWOiYiIIC4ursLz0r59e4Byx1itVgYPHkzbtm158803vY7ZunVrmb9L5zj5ii5duuB0Ojl37hwtW7b0+KlKiO1SMJlMHknnoIRjDx06RExMTJl51HbZhJqmVgXT4sWLmTRpEs8++yy7du2ib9++3HrrrZw8edLr+A0bNjBw4EBWrFjBjh07GDBgALfffju7du3y88yrR2013lVR86YUwaQsodUFB5f5oN5+djt2l52E4AQSQxP9Pc0ap6LEb7Vgo959DrRVcg1AMGmlBdzFK1WHyYiyXRdgwOgWTLLswunDxG/p4rICNSyY1JCc0y14c93vtaqWFNDm5X4d1PWyAhkZGdxwww18+umn/Prrrxw7doyvvvqKWbNmMXjwYG1camqqh9DZvXs3mZmZzJkzB5vNRkpKChs2bODUqVOsXLmSgQMHkpCQwMsvvwwo4bjbbruNzp07a45McnIyQ4cOpVGjRnz66ade59e4cWMmT57MO++847F9+fLlHuE4UGoszZ8/nx07dnD8+HFWrFjBM888w4ABA7S8I1CSodPT0zl9+jQ///wzU6dOZfz48Tz66KOaKzR//nweffRRfvzxR44cOcK+ffuYOnUq+/bt4/bbbwfg0Ucf5aWXXmLTpk2cOHGCrVu3Mnr0aBo1aqSF1b7++msPsfPII49w6tQp3nnnHc6fP096ejrp6ekeycybNm1i1qxZHDx4kH/+85989dVXTJw4sdrPbXVp3bo1I0eOZPTo0SxdupRjx47xyy+/8Nprr7FixQqfHDMxMZFff/2VP/74gwsXLmC32xk5ciTR0dEMHjyYjRs3cuzYMdavX8/EiRM5ffq0T+ZRW9SqYHrzzTcZO3YsDz30EO3ateOtt96iadOmvPfee17Hv/XWWzz99NP06NGDVq1a8fe//51WrVrx7bff+nnm1aO2SgqoaKUFip2lEr7L5i9tSt0EQJ/4PvUqp6M8Sid+X4y6nF7vdpishe7WNfW48a7KxaUF1A93gyqYAo0YTCVhJ4cvw3I+dpjUApwut2DKvsS2KPXl9R4cHMw111zD7Nmzuf7660lOTuZvf/sb48aNY86cOdq4119/nS5dunj8LF++nFatWrF9+3aSkpIYMWIESUlJPPzwwwwYMIAtW7YQGRnJ2bNn+f777726N5IkMWTIkHJrMgFMmTKlTEho+fLlHoIOICUlhUWLFnHzzTfTrl07nnzySVJSUvjyyy89xn3wwQfExcWRlJTEXXfdxf79+1m8eDFz587VxvTs2ZP8/HzGjx9Phw4d6NevH1u3bmXZsmX069cPgJtuuomtW7cybNgwWrduzdChQ7FYLKxZs4aoqCgAcnJytNVjAOvXryctLY327dsTFxen/WzevFkb85e//IUdO3bQpUsXXnrpJd544w1SUvyzYGbhwoWMHj2av/zlL7Rp04Y77riDn3/+WUusr2nGjRtHmzZt6N69O40aNWLTpk0EBgayYcMGmjVrxpAhQ2jXrh0PPvggRUVFHsK3ISDJtfSVqri4mMDAQL766ivuuqukOOLEiRPZvXs369evr/Q+XC4XiYmJPP300zzxxBNex9hsNo/lt7m5uTRt2pScnBy/PZk/H81gxPtbuapREP/7S3+/HLM0/f+xluMZhfxnfG/apf3ByT8/iLlVK676drnHuNu/vp3jucd5q/9b3Nj8Rr/Ps6Z56IeH+Dn9Z17t+yq3XeVZo2bV+3P4dc1K2uTZSDp6mu13ziM328ldf+lCfKuIWppxzbDszZ2kHszm5rEdaNWjMdu2bWPFihUkBSQwIKstEcNaE9StMbPvG4zL6eTh9z4iJDLaJ3OxXyji7Ovbtb8b/6UbxkYVN32uDq5CO2deVEIiCTOv5T8Xsnny95NcHxHMl1e3LHc/q9XKsWPHtHSAzMxMrFYrYWFhDSLXoi6xc+dObrjhBs6fP18mJ6e+k5iYyKRJk5g0aVJtT6XBcPF7szS5ubmEhYX59fpdmlpzmC5cuIDT6aRxY8+VWI0bNyY9Pb1K9/HGG29QUFDA8OHDyx3zyiuvaEtDw8LCfKa8K6LQXQMp0M9VvlVKlxYor6TAhaILHM89joRE99jKV3TUB9SQnDeHyeHOYVKTJYvdPdYagsNkLM9hcrnborhLD6guky8dJl8XrpQsBnAfwlXoKFW0snqh1foSkquPOBwOreq2QFCfqfWk74utcG81c7zx+eefM2PGDBYvXlxhKffp06eTk5Oj/Zw6deqy51xdNMFUjWXONUnpkJwzvySHqTQ7zu4AoHVEa8LMDSNRr2LB5M5h0huQAZtNuVA2hBwmtXmw3eaZw2RwevaRM7iT/n1aWuDiVXI1LZh0Ejq3yHUV2slR+8g10Bym+kjPnj0ZNWpUbU/jiuPWW2/1KDdQ+qeiGk2C8qm1q0N0dDR6vb6Mm3Tu3LkyrtPFLF68mLFjx/LVV19x000VL383m82YzbXbPLawWPmm7+8+cioBpR2mfLfDVI5g6ta4m38n50MqEkzqKjmDwYBTb0a9Ttb3wpVQymEq8nSY9A5FFKgCQ0389mVpgTJlBXyw8EEXZMBVYMdZYCfHVf22KCAEk+DS8NYqpq7w4YcfllkNqRIZGenn2TQMak0wmUwmunXrxqpVqzxymFatWlUmObA0n3/+OQ8++CCff/55lXon1QXUtii1FZIracBbftJ3QxZMXlfJqWEqkwmHQTkXOoOEwc+V2H2B6jAVux0mLSRnVwXTRSG5Yl8mfZf8Kpn0ZQRUjRwi0AgU4Sqwk20sKStwKQjBJGgoJCQk1PYUGhy1Gn+YPHkyo0aNonv37vTu3Zv333+fkydPMn78eEAJp6WmpvLxxx8DilgaPXo0b7/9Nr169dLcqYCAgDpd76Ekh6l2Trel9Co5d0hOX8phKrAXcChL6T3UtXFX/0/QR1QYklNFhNGE3S2YzIHGerNaqiJMF/WT00JyWlmB2nGYJB+JUV1QqZBcUPUcpourWwvBJBDULnX5PVirgmnEiBFkZGTw4osvkpaWRnJyMitWrKB58+YApKWledRkmj9/Pg6Hg8cff5zHH39c2z5mzJjLbgvgSwrdBQRr22Eqsru8Jn3vz9iPjExsUCzRAb5ZLVUbqGUFvDlMakhObzLhMCqCqb433lUxmssvKyBZ9FptJK14pS8dJr3vBZNeFUwFDnJMVavDpCYgFxYWEhAQ0CCEskDQENBSCKrYX9Gf1PoV4rHHHuOxxx7zetvFImjdunW+n5APqO1VcupxPQpXBpU4TL9d+A2AjtEd/T85H1JRHSY16dtgtmAt5TA1BEwB3h0mo6zX3BhA6ydX7x0mNem7wE5OiDvpu5JVcnq9nvDwcK0vFyiruWw2W6VVpgUCgW9wuVycP3+ewMBAj5Y8dYW6N6MGSKE7h6m2k76tdidOLw6TKpg6RHXw/+R8SMU5TIpIMFrMWg6TOahhvB0qcph0pURh6fYoPqO0YPJRH0Wd+3lzFtpLlRWo/L2mto84d+4cNpuNoqIijEYjmZmZPpmnQCCoHJ1OR7Nmzeqk69swrhB1nKK6Uoep2PsquSvZYdKbA3AYG05bFCiV9H2xw4QefanH6Jekb8m/DlO2Q8l/qEpZAUmSiIuLIyYmhj179rBp0yaaNWtWpoWHQCDwHyaTCd0lNKT2Bw3jClHHKSkrUMt1mLysksux5XCm4AwA7aLa1cr8fEXppO+L63uprVGMFgt2Q8NpiwJgchemtF/sMMmeDpMWkvOhYJL84jApj8lWYKfQXWuqOq1R9Ho9JpOJ/Px8CgsLy1QXFggEAqgDhSuvBNQcpqBaT/ouu0ruYNZBABKCEzSB0VAINSsOk1N2Uugo9LhNy2EKCNBCcg0m6bsch0kJyZVymPwRkiuFr5O+c2wlTYRDq5kwquZLOHzYiFggENRvhGDyA7Wd9K3lMJUqK6DmMKmCqVVEq1qZmy+x6C0YdcrFNNfmmcek5TAFBGI3BgANx2EyB3jPYTJelMOklRXwZUiuFL4LySmPN9MtdiIMegzVrPekCiZVXAoEAsHFCMHkB1TBVFshOYu3kJzbYVLrL7WOaF0rc/MlkiR5Tfx2OZ3ILqV3nCEwoMElfZvclb5dDhmH3VniMMk6LUEa/NNLrjS+qPINJSG5LPenWcQltCBSywwIh0kgEJSHEEx+oKi4btRhshbbcRUqoSnVYVIFU0N0mMB7LSa18S6AMTAYu0E5F5YG4jAZS/VrK8ovxqWKw/IcpnoekpPMetBJZBsVVynyEoSZKpiKfdlXTyAQ1GuEYPIDmsPko2/YlaEe11lQksejCw7GJbs4lN1wHSYoRzCVuigag4OwG92CKbhhCCZJJ2l5TAV5Jc+58eIcJj/UYfKYl4+SviVJQhdoIMekCqbqO0wmdyNiIZgEAkF5CMHkB9SyAkHm2lolpzzNUqESjsNgQDKZOJN/hiJHEUadkWYhzWplbr4mxFy2PYoqmPQGA7qAAK3Sd0MpKwAleUz5uYpg0qNT/pVymEwBSu6W3VpY9g58gK8cJlDCciUO0+UJprrcmkEgENQeQjD5GFmWKajlkJxahwl3OE4fFIQkSRzLOQZA89DmGHQNRyyURnOYSiV9a21RjCYwlRSutAQ1DIcJwOjOYyrIV7qVm2Tlb31wWcFUXE5H85rGp4Ip0EB2DThMsiyLPCaBQOAVIZh8jM3hwuX+wlrblb6lIs+Eb1UwtQhrUSvz8gda8Up7KYdJTYI2mbBLJTV3GpLDpBavLCxQxJBRdjfe9XCYFKHoL8GkCzb57r4DjZeVw6QKJhBhOYFA4B0hmHyMGo4DCKylHKZA9+o8/UUJ38dzjwOQGJpYG9PyC94cJrWkgMFkolhSLpQGpxWdvuG8HdSQXFGh0hfNhAEpwOCRR2SyqA6Tb0Ny4Xe2JCA5iqDujX12DH2QscRhuoTVqDqdTistIASTQCDwRsP5Sl1HUfvImfQ6DLV0QQ50r5oy2RWhcCU5TKWrfas4i91tUYwmip3KW8Do8E8ej79QQ3JWt2AyynqPcByUCslZfeswBfeKI7hXnE+PoQs0kF2kCKaoSwjJgeIyORwOIZgEAoFXGs5X6jqKVlLAXDvuEkCQ+xt3oF25eKoO05UgmLytklMLNRpNZopdyvNiKM73/+R8iCnAXXvL6n6sGLR6RSVj3CE5q1WrS1Vf0QWWOEwR1WiLUhqxUk4gEFSEEEw+psDmrvJdS+E4AL1OIsCoJ9ChOkxB5BbnkmHNABp2SM5b4Uq7TRGOBrOZYpciIoz2fFwN6EKpFq+0WUtCcvoQzxwi1WFClrVzUl/xyGG6xAKxQjAJBIKKEILJx5RU+a49wQRKSYNAR4nDdCLnBACNAhoRbAquzan5lDBzGHBRDpO77pDRbKbYobwFDPZCrW1MQ0BtwGuzuduiyPoyDpPBZEaSlMfvr8RvX+EI1FNwGWUFQAgmgUBQMUIw+Zgiu1pSoHbTxYLMegLcDpM+KJgTeYpgahbaMOsvqaiCKac4R9tmLyWYbEWKoDU6ChqWYFIb8BaXhOQuzmGSJAlToH/ymHxNjln5KNPJMmEiJCcQCHyAEEw+ps44TCZPh+lk7klAqcHUkAk3hwOQY8vRChKq4Sej2YK1QEkAN9obmGByO0x2h3LxN8kGdF4qmZss7jymwvqd9K4KplA76KXqNd5VEYJJIBBUhBBMPkYVTEG1LJiCzQYCS62SO5HrdpgaaIVvFTXp2+6yU+RQXBQ1JGcwm0sEk6MQZ0MSTO4cJru7SKcRPbqgsnWQ/LVSztdkuw3csGIXst1Z8eByEIJJIBBUhBBMPqbQVvdCcleSwxRgCMCoU5yVHJsSltMcJpMZm1swGewFuAoKameSPkBdJedwKY/PJJcNySnjFMFk83EtJl+TISmr/KKKZZzu57S6CMEkEAgqQggmH6PWYartkFxg6aTv4CBO5imCqaHnMEmSVCaPSSsrYLFgLVAErRKSa0CCye0wqYLJSDkhOXdpAXs9T/o+73Zyo2wyrnwhmAQCQc0jBJOPUSt911YfOZVgk0FzmIqMJcvsm4Y0rc1p+YUwkyKYsm3ZQKmQnMlMUZ47x8eej6ugAYXk3DlMLpdbEKJH76VXnr/7yfmKc+5ipMJhEggEvkIIJh+T7w7JBZl9FJJzOqC4ECrpsB5kNmiFK8/plKrXMYExBBgCfDOvOoTmMGkhuZLWKEX5atJ3foNK+lb74rlQXn8mndIapew4pYiprbB+u2vn3AViL8dhMpvNANjcrw+BQCAojWiN4mMK3IIpuCYFk8MGR9bCns/h0CqwF0BMe/jTbGjWy+suweaSwpVpsiIcGnr+kkp5gkmnN+G0K7kvRnt+g0r6VgWTLCmvP0tQAJKX1WOWYKWwZ1F+Xpnb6hOaw2Rz4bpEh8liURoxW631u4inQCDwDUIw+Ri10neNrJI7tgG2faCIpeKLLnDn9sNHf4I//xea9iiza+kcplRXJtDwV8ipaMUr3WFINenb5XI3JZZc6J22BpXDZDDq0RvBpXO//kK8Fye1BCnbrfVcMJ1XHabLCMkJwSQQCCpCCCYfUyMhOacD/jsFtv+rZFtwLCQPgY7DICQWvp0Eh36AL0fD41vBEuZxF0FGCYtTuZCccJ4Droz8JSiVw2TNBkpymGR3Hzmz0YkEDSokB2AMLPk9IDTQ65iAEKXsQn0XTCUO06WH5ALc+VxF9TyfSyAQ+AYhmHyMKphCLJdxqv/7tFssSdD9QegyEuK6gK5UCtrdC2D+9ZB5FDa9Azf+zeMuQl0liazH7OnAlROSC7eEA2VXyTkdekDG4i5P5MzN9bJ3/UUfKIOslBQwhFq8jrEE13+HySnLXHA7TNE2WYTkBAKBTxBJ3z6m4HIdpr3/ge0LAAmGLYQ/vQkJ3TzFEoA5BAa+pPy+5Z+Qf87j5mB3/pJDZ+BY0WngynGY1OKVag6Twx2Sc7r7yFmCFKfJmZ3t/8n5EL1Fyc8yy8YyjXdVLMHKuSnKq7+CKdPuwAVIQLhdxpl/aavcSgsmuZJFFAKB4MpDCCYfc1khuYwj8O1E5fe+f4EOd1U8vu1tiphyFMGuTz1uCnIqFxGryawtr79SBFN5Sd+OYuXlHxCsiImGJph0JrdgwoA+tDzBVP8dJm2FnF6PQeaSHSY1JOd0OrHbL+0+BAJBw0UIJh9zyavkCi7Av0dAcT40vw76T698H0mCHg8pv+9cBC6XdlOAO+G70N3JvVFAIwKN3vNaGhoXCyY1h8luU1aNBYQrzoIzK6sWZuc7JKOS8G2WjejKcZhK5zDVV1cl3aaIm0bu1/blFK5UVxKKsJxAILgYIZh8jLpKrlqCyZYPnw6FjEMQ2gSGfgj6Ku7f/k4wh0HWcTjxk7Y5wK4KJuUpv1LcJYAIcwQAWTZFEKmr5IrdgikoUnFZnDk5yKVEZr3H4BZMVBSSUx67y+nEXk/7yZ2xKe5pQoDyGGW7C5f7i0p1kCRJ5DEJBIJyEYLJh9gcToqdygW4yiE5WYYlYyFtNwRGwehlEBpX9YOaAqH97crvv3+nbVYFU4H7upkYllj1+6znRAVEAZBlzcJuL8bpUC6mxUXKyz+wkVKLCJcLVwNK/HZJitNilg3lCiaj2YLBqNxWX/OYUq3K42wSaEYyu/PRci8tj0mslBMIBOUhBJMPUd0lqEYdpl8+hIMrwWCB+76C6FbVP3Bbt2A68L1WAdziFkxFZkXAXSk1mADCzeEAyMicz0nXttsKFYcpMDwAndtpcTSgsJzLvTLSjBGdl7YoKvU9jylVdZjMRi1Xy5lz+YnfAoFAUBpRVsCHqPlLFqMOg74K2jTjCPzoLgcw8CVo0u3SDnxVPzAGQe5pOLMLErqid3ejL7Ioc0oMTby0+66HGHQGws3hZNuyOZ+dBoDeaMRaoAhaS4gRR0QErvx8nFnZ0KIWJ1uDuNwrIw0YkfRlq3yrBISFk5+VSUFO/RSLqsOUYDGhDzPjOF+EM7d67U2Kiy+QkbGRuPiVNIpJ5UzaKi5c0GE2N8YS0JSI8J5ERvYlIKCJLx6CQCCoBwjB5EPytYTv8r/da7hc8M3jygq3Fv1KkrcvBWMAtLwBfv8WDq+GhK5a248ii3IhuVJqMKlEWaLItmWTkXMWAFNAIIXusE1QmJmi8HDsp07hzK6fosEbNrerqMN7OE4lOCKS88ePUlBP3TU1hym+tMNUxZBcfsEhTp74gPSzy5FlO4HudRAuF1htYLWdISd3F2fPLgcgLKwrCfH3EBPzJ/R6c80/GIFAUGcRgsmHlKyQq0I47rclcHKL4gwNnlO2zlJ1SbpREUxH/gf9nsaVpwomJxIGmoZeOUnfAJEBkRzJOUJmznkAjOYAih1KuDIwzIQ+IhwAZ2ZmbU2xxrEWK66izlWxYAoKjwQgPyvD53OqaVyyzJnSDlOoImKcORU7TE6nlaNH3+TkqX8ByusgJLgDublN+P13K61bd6NXrz7YbGfJLzhIZuYmcnN3kZOzk5ycnRw+MotmTR8kIeE+DIYQnz5GgUBQNxCCyYfkVbUGk90Kq2cov/edDOE1kF+UNED5/9Q2sObiKnALJrNEmCkG8xX27TjSooiCnDxFFBhMFoodEBBiRK/XYYiJAcB+7ly591HfKHI7TC5HxQ5ncKRybuqjw3Sh2EGxLCMBsSYjtrDKHab8/IPs/e0xCguPAdAoeiDNm48nLOxqtmzZQkbGD+TlNSYsrAsAMdzCVS0mYLOdIy1tCamp/8ZqO8PhI7M4fmIezZuNo2nTscJxEggaOCLp24dUucr3nn8r+UYh8dD78Zo5eEQiRCaB7IRjG0pCciYIN1Rj1V0DQRVMefnZAOgMSnJvULhykTPGNAbAcbZhCCan04nV6S7QaTPgdJZfLiE4QnWY6p+7luquwRRrNmLUSZWG5LKzt7Nj5wgKC49hMsXQudMHdOo0j7CwqwEICgoCIN9LX0GzOYbExEfp3ft/tG83i8DAq3A4cjly9A127ByB0ykSxQWChowQTD5EFUwhFQkmpwN+ekv5/doJSv5RTZF0g/L/sQ248gsAKDRDiD6h5o5RT1AFU0GBUrxSp1MurEFhimAyNFYF09lamF3NU1CgPN+SDHaXgaIKHBc1JFdQD0NyJ62KKExw5wmqITmXl5BcRsZGdu0ejcORS1hoF3pds4Lo6Bs8xgS7Vwx6E0wqOp2RuLih9LpmJR3av4nBEE5e3l4OHnqpRh6TQCComwjB5EPyrO4cpooa7x74DrJPKDWXuo6p2Qm06Kv8f3wjLvcFoNAMFq48h0mtxVRYoC6dVwWT8r8xVhFM9gYimNQLvgUTVpdEfnb5OT2aw1QPE96PFSqPq0WgIpT0akguvxjZVVK5PC/vd/b+9jgul42oqAF06fIJRmNEmfurimBSkSQ9sbGDSU5+G5A4c+YLMjM3Xe5DEggEdRQhmHxITpESLggLqCCH5Of5yv/dH1SKTtYkiW7BdG4/rtxsAIrMoHfE1uxx6gGqw2QrVJwXl6w8J4HhDdNhyncXoQyQTRS5ZAqzy3eYgqOiASjIzNSKetYXjhUpj+uqAOV51AWbQCeBqyQsZ7Wls+fXh3A6C4gI70WnjnPR6707uapgslqtVe4nFxV5HU2a3A/AgT+ew+GoXGwJBIL6hxBMPqRSwZT2K5zcDDoDdB9b8xMIjITGHQFw5Cj5KUUmxiX/aQAAKNNJREFUCdkWU/PHquNEWRSHqdjd/sPlVJ6Ti0NyzsxMXMWXVvSwLpFzPhtQBJNNhoIKVo0FhUdgMJmRZRe5F+pXDtexIrfD5BZMkk7CEKH87sgoQpZl9u2bjM2WTmBgSzp2fE8Lx3ojICAAk0m5PbsazZiTrvoLZlNjioqO89u+SdjtVd9XIBDUD4Rg8iGVCqZfPlT+b3dH9dqfVAd3WM7pXiVXYLCQW2jxzbHqMGpIzuGu4Oy0K6Ue1KRvfXg4ktl9oU1P93IP9Yuss4pADtQprmVBBSE5SZIIcye9Z6en+X5yNcjRi0JyAPooxT1yZlg5e+47srN/Rqez0LnT+xiNoRXenyRJhIeHA9UTTAZDCB07zkWnM5GRsZYNG7uxbn1nNm+5kT8OvqCtyBMIBPUXIZh8iCqYQr0JpqJs2PuV8nvPcb6bRNNrkGWQrUpV6wKpERfyqlcFuSEQGxiLhIS+WMlrsRe7BZM750WSJEzNlNpUxSdO1M4ka5DsTCUfKcikrPqqSDABhMfGK/udrT+CKdfh5IJdCSGqDhOAIUr5QlCcmceRI68DkNj8UQIDq1asNSLC3ay5mmUWwsKu5urOHxEc3BYApzOfoqLjnD79MVu23sy+fZM5f34VaenLOHbsXU6f/gybrWGEgAWCKwFRh8mHVOgw/fol2AuhUTto1tt3k2jWC9khIbnzX/PlWPLyrzzBZNQbiQmMwexOS7HbjOhNEBxR4raZEhOxHTpM8fET0LdvLc20ZsjJVVYDhgQpjkpFITmA8MZKXltOPRJM+/OV8GqC2UiooaQ4rMHtMF0o/AGr6TRGYxTNmlU95H2pgknZ9xp69vgOuz0LhyOXgsIjpKZ+TkbGWtLPfkP62W88xh85+iZXX/0vwkI7V/tYAoHAvwjB5EPKFUyyDDsXKb93/zNI5ff5umxCYnEGNAPsFOuhyNEEu81BUbGTgKo2BG4gJAQnYC52CwIpAINZT0BIyXNjaq44EMXHj9fC7GqW3CIl6TssOhIooqCSZrThjZWQcFY9Csn95hZM7YM9E7gN0QHIkpP0gC8AaN5sbLlJ3t64lJBcaSRJwmSKxGSKJDAwkUbRN5Kbu5fU1H+Tm7sHgzGcgICm5ObuoaDgEHv2jKV3rzUYjWGXdDyBQOAfhGDyITmF5Qim1J1w9jcwWKDTcJ/PwxGaDOwiPwD0jibYgQv5NppG1vCqvDpOfHA85mIlBCJJAYRFW5BKiVVTYiJQ/0NyTqeTfIciJqKbNAJOVhqSi2qihCMvnDzu49nVHKrDlHyxYIqykBu3mWJzGkZjBAkJI6t1v6rDlFmDbXJCQzsSGvqKxzaHo4DtO4ZSUHCIY8feoXXrv9XY8QQCQc0jcph8hCzL5TtMuz5W/m93BwSUrQVT02QGKIUq8wIgyqS4KOeuwDymuKA4LO5kbyQLodGeF1rNYTp61N9Tq1EyMzKRkTHIOhq1VpK5bYUO7MXOcvdplHgVALnnz1GUn1fuuLpEeQ6TLkzHhaRlADRt/BAGQ3C17rdRo0YAXLhwAaez/HN2uRgMQbRupYik06mfUFBw2GfHEggEl48QTD4ip8iOw104Lyq41DLm4kLYu0T5vesov8zltF15mh0WaBysJAGfz7vy2jjEmxujdymOkqQLKCOYzK1aAWA/cwZnTo7f51dTnD1xBoBwOYiAxkGYgxQjOTu9sNx9LEHB2kq588fr/oouq9PFgXzlNdwxxPN5TDu3BEdABnpbGDHy4Grfd3h4OEajEafTSUaGb6ufR0ZeS3T0Tciyk4OHZiLLcuU7CQSCWkEIJh+hOjjhgUbMpRJS+X05FOdBeHNofp1/5uJOANabnHS0nPOY35VENEqOiCzpACOh0Z7lFfTh4RibNAHAun+/v6dXY5w9oeQhRZrD0Ol1RDcJAeDC6Yqdo5jEJGX/Y3Xf6didV0ixLNPIZKC5peQLicORz7HjcwCIOno7rvTqF+LU6XTEuJsxn/NDM+ZWLacjSUYyMzeSkbHO58cTCASXhhBMPuK8W5A0Cr6og/muT5X/rx4JOv+c/gvnlJycIJOTrrojAJzMKN9taKiEO5ScLVlnRpKkMg4TgKVDBwCs+/b5dW41ybk0JU8rOkKp4B3dVAlJXThVcQXquNbKcvjT+/f6cHY1w7YcpWL7NWFBHnloR4+9RXHxOczEE3a6H8Wpl1Z1OzZWWTWYmpp6+ZOthMDARJo2fQCAg4de4tz5Hzhy9E32/Powx47NwW4v3+20WtNIP/sthw+/xqHDr5CRsUG4VAKBjxBJ3z5CE0whpQRT1nE4vhGQ4Or7/DIPp8tJwXklRBNtsBPrPAB04PgVKJgCrIpA1UmK4+JVMCV3IO+HHyja86tf51aTpGUrrkhcU2XlW6MmbsF0umLx0Lzj1QCc2rcXp8OB3lB3Px42ZymPpVd4SX5SXt4+Tp1SVp8mRU/DKRuxX6JgSkxMZMeOHRw5cuTyJ1sFWiQ+Tnr6MoqKTrB372Pa9gsX1nDy1EJCQzpgd2QTEtyBgMBECgoOkp29Hav1tMf9nDz5IWGhXWjZajphoV09xKRAILg86u4nYj3nbK6SXxFTWjDt/rfy/1X9ILypX+ZxKPsQodlK8nljk52AnJ3AUI5nFPjl+HWJQnddHZ0UCpJcJiQHENSjB+eBgm3bkJ1OJH39Kr2Ql5dHrrMAZGjeXknkjlJDcqfykGW53Itoo2aJBISEUpSXS+qB/TRL7uS3eVeHPIeTTdmKEOofqTw2WXZy4I+/AS5iYgYR0+Jm0tiKI8OKy+pAV1EDbC8kJSnhyXPnzpGbm0toaMUVwi8XgyGEbl0/5+ChmeTl7SM0pCPh4T04k/YVhYVHycxSmvrm5V3sfOoICWlPaOjVyLKd9PTl5OTuYseO4VgsTQgJ6UBwcFvCwroSEd4Dnc5c9uACgaBKCMHkI05kKg5OM3XpvtNREo7r4p9kb4AdZ3cQlatY9KYgmcC8YzQmk5MZOpwuGb3uyvkGmp+pJPBKumBsYXkYjGXFkCU5GV1oKK6cHKz79hHQqW6KhvI4tk/JP4ogiJDmSjuYiLhAdAaJYquTzLQCouK9rxqTdDqSuvfit7U/8vtPa+usYFqTkYtdlmkVaKZloCJ6jx57h9zcPej1wbRu9Vf0ZiP6CDPOLBu2E7kEtIms1jECAwNJSEggNTWVAwcO0LNnT188lIuO2YKrOy/w2NakyRiysjZht2ej1weSk7OT4uIMLAEJhIf1ICysi8cqwBYtJnD06Fukpy/Daj2N1Xqa8+d/AMBgCCe28e3ExQ0hJKSjcJ8Egmoicph8xAm3g9MsSlmVxuHVkJsKgVHQ7na/zWNj6kaic5Xfjc2Vb819jb9T7HRdcS5T7oXzAEi6EM5YjuCSXWXGSAYDQb16AZC3eo1f51cTHPhVSVZvGhSLZFDe3nq9jmbtFfH0++aKC1N2uP4GAP7Y8hO2wrr5+vg8TamPNKhROAAZGRs4fvyfALRt8xJms7Laz9JaKdlh3X9pK906uPPZ9u6tvZwuvd5MdPQNxMUNISbmFlq1eoYOHd4g6arJREX1LVMywWKOpX27V7m+7w6uvnoRrVo+Q2zjOzGZGuFwZHM69RN+2X4XW3++heMn5mO11f++iQKBvxCCyUccv6A4TIlRbofp53nK/53vBYN/bPFMayZ7jm4hVClXg7GDsirvtqADAOw+me2XedQVMlOVfA9JF8FpyxG+PfKt13GhgwYBkPP118h2u9/md7nY7XaOpB0HoG3bNh63deir9Io7sCUNu6382kIJ7ToQmdAUu7WI7d8t89VUL5k/Cqysz8pDAu6Li6So6CT79v8FkElIuI/Y2Du0sQFukVi0PxPZVf1E6OTkZHQ6HadOneJ4Pav+bjAEERV5Hc2ajaVDhze47tpNXN15IY0b345OZ6aw8DBHjsxi06br2Lnrfk6eWkhhYf0u2CoQ+BohmHxAVkExqdmKSmkVEwJndsPRtSDpoefDfpvHquOrSDivXBwNjRuj73InANfZNhJJLjtPVr9XVn1FdrnIPOMWTPooLgSfYvaO2VwoulBmbMgNA9BHRuI4f57sr7/291QvmV+37cYm2wmSzbTs29HjtmYdogiJsmArcPDjh7/htJd110Bp69FnmFIZ+5fl/6lzlb9fOaosYLglOowYzrFz1yjs9kxCQjrQquVfPcaak8KRzHpcecXYjla/rlZoaChdu3YFYNWqVT4tYulrJElPVNT1JHd4i77X/Uzbtn8nLKw7IJOVtYVDh2ayZesNbNmawqHDr5KV9TMu15VXekQgqIhaF0xz586lRYsWWCwWunXrxsaNGyscv379erp164bFYuGqq65i3rx5fppp1dl9OhuAq6KDCLPoYJW75UHyEIioWsf0y6XAXsC/fvsXiWeVb9bm1q2hWS+IuxqjXMwYww+sPXAO1yV8866PXDh1AkexDTCg04cREmskw5rBo6sfJcvqKRwlk4no8Y8AcP7N2RSf9v3S8svFZrOxbt1aADqGtsQUcVH1a53ETX9uj96o4/jeDP4zazuZZ7yH3Fr3upYWXbrjtNtZ8uoMMk6f9Pn8q8LnaRmsvJCLDngsKo1ftg/Baj1NQEBzOnf6EL3e07mVDDoCr1aqdmcvP4yrgkrn5dGvXz9MJhOpqaksXryYvLz6UQW9IgyGEBLiR9C922J69/ofrVo+Q3j4NUiSnsLCw5w8+QE7d93H+g1d2LHzXo4ceYOMjA0UF9dcqxiBoD5Sq4Jp8eLFTJo0iWeffZZdu3bRt29fbr31Vk6e9P4BfezYMQYNGkTfvn3ZtWsXzzzzDBMmTGDJkiV+nnnFrPldqYPTo3korJwGxzaAMRD6TfXL8dML0nl6w9OcKThDz5PKRSSwW1elyW+fJwF4Qr+MHnmrWfuH7wvz1QWO79kJgM7QhGbto3nn5reJtERyIPMA931/H5tTN3vUr4m4914s7dvjzM7mxMiR5K1eXWfDc0UFhfz7vUXk2QsJli1cd9cNXsfFtwzntkc7YQkycuFUPov/vo2NXx4k53yRxzhJkrjl0UlExjchP+MCn06bxIbPFpJ9tnbyXZyyzHsnz/GXA6cAGB20i9wDbmcpuANdu/4bsznG675hKYnogo04zhVx7u2dFO45h+ys+peEkJAQBg8ejCRJHDx4kPfee49du3Zhr6OvheoSGNicZs3G0q3rv+l73XaSO7xNbOPBGI2RuFw2srO3cfzEXHbv+TMbf+rBxp+uYeeu+zlw4K8cO/5P0tK+JitrK4WFJ3A6raIGlKBBI8m1+Aq/5ppr6Nq1K++99562rV27dtx555288sorZcZPnTqV5cuX8/vvv2vbxo8fz549e9iyZUuVjpmbm0tYWBg5OTk1u1TY5YS8dE6dz2Lqx2to7TzMX2J2EJLlXgY85EPoNKzGDud0Ocm0ZnK+6Dwn805yLPsYB7MOcjj7MMdzjhFeAO1P65j4jRPJ5aLFN99gadMaXC5Y/gTs/gyArVJnAjoNplnzlkRExUBAOFjCIDAaDKaKJ1EPsFutnDl0gP/OeZOC7EwMgTdy66P30rpnLEezj/LYmsdIzVccpITgBDpFdyI2KJZQcyghWcW0fuFzLKlK2E4OCkDq0AZDfByGmBiCG8VjCo9EFxiAZLagM5uQLBYkkxnJoEcfHo4hKuqS5i07XDjz7cgOF7LNiVzsxOlwkleUR5HdRmFREQW5eZw6cYqDZ49ixY5e1jGs0y20HVrxiq6CbBtrPzvAib0lydBhMQFEJQQTEmHBEmzAEmxCr7ey678LSDtUUpMqtFEM0c0SiYhLIDgikqRuPYmIS6j243M48rDbc5FlB+DCJTtwuJyctDrItDvJtLs4X2xjf4GdtXlmzjmU12J/eTVjmYcOmYSEkbRqOR29vmw9rdLYjuaQ8cUBXLnFAOhDTZhbRWCItCCZ9UgSoNehC9CjDzGhCzKiCzCiCzIi6ZWVZGlpaSxbtoyzZ5UvQ0ajkejoaJo0aUJCQgIdO3ZEX89KUFSELMsUFh4lO/sXsnN+ISd7J0XWyp1GSTKg1wei1wdhMASj1weh1wdi0Aeh01vQSUYknQmdzohOMim/S0Z0OhOSznjR76ZSvxtA0iNJeiT0SDr3/5IOSTIo2yWj+38DBkMwRmOYH86UwJ/47PpdRWpNMBUXFxMYGMhXX33FXXfdpW2fOHEiu3fvZv369WX2uf766+nSpQtvv/22tu3rr79m+PDhFBYWYjQay+xjs9mw2Upi8bm5uTRt2rTmT3hOKsxuX3a7IQAGz4GOd9fcsYCfUn/i0dWPer0tIk9m/pyS8EPIwJto8u67JQNcTmyrXsSw5R30eM9l4Z5/Q9vbanLKfkeWZeaPH01BthJyk3ThhDT+Mw/M6o/RpFzccmw5vLfnPZYeWkqRo6jMfVhsMndtcXHjbllLnq8qkQ88QONpl+Yq2o7ncH6eZ/HMbKmA/5i3eh0fRiC3972VpBuSq7RcXJZlTv2eye7Vpzj1eyaU8ykgyzIu+1Gctl3IrlRkl2dYa/D//ZWWPXpV7UGV4sjR2Rx3tzBRySGMx6R/eR0fLOcynH9zk7SeRo1uonnzRwgNSa7y8VxWB/mbzpC/+Qyugqq5QzFPdsGUULIKzeFwsHXrVrZt20Zubq623WQyMW3aNHR+qtxfWzgcBRQWHiG/4CDWotNYrWew2tKw2dKwWs/UqZyn+LjhtGtX9ku3oH5T24Kp1uowqZ3AGzdu7LG9cePGpKd7t/7T09O9jnc4HFy4cIG4uLgy+7zyyiu88MILNTfx8jBYQGfEqTeT4QwgvFkyptY3KqvigqJr/HDRAdHoJB2Rlkjig+NpEdqCVhGtaB3Rmlbhrcj46Fb04eEEXXstMVOmeO6s02NOeYG85Pv49fv3MJz/jSB7Jh0iZSRrDhRlKy5TPUeSJBpf1ZL0o0ewFTUmvs2tXH9vN00sAYSZw5jWcxoTukxg+9ntHMk+woWiC+QV52F1WLE6rRxpXsD+IUWEH7tA6JlcQjKtBOc7uCXyWkz5xchFRbiKi5GtVlw2K7LVBk4nusDAS5+7QQd6CcmgQzLr0Zn0BEl69Hk6LJJJ+dGZiA6JJKl1Szrc0BW9uepvZ0mSaNY+imbto7AW2Dl3Ipes9EIKsm3YCuwU5tkpzLFhLbBjK2xDcVESI/56NYU5qVw4eZycc2cpyM66JHcJQKczodNZ3I6A8hOJkUBHISEUECoVEioV0cRQQBdLFteF2IkKvYVG0f/AYAip/vEsBkJvbEZIvyZYD2dTfCoPV26xktcky8hOGVehHVeeXfm/yIEuwPN8GgwGrrvuOvr06UNGRgbnzp3j1KlTuFyuBi+WQFl5FxraidDQsvW5ZFnG6czH4cjH6SzE6Szw/N1ZgMtlQ3YV43LZccnFyC47LlcxLtle6vdiXK5iZNmujHMVI7uKkXEiyxX9ODx+FwU6Bb6g1hymM2fOkJCQwObNm+ndu7e2/eWXX+aTTz7hwIEDZfZp3bo1f/7zn5k+fbq2bdOmTVx33XWkpaVp/Z9K4zeHqRQVVVOuKVyyC1mW0eu8hwFkux3Ji+NWHjaHs6RJsCwrPw3gImC3WTGaLeRmFBEaVXHopq6jvlVro+BgbR67NrjSHq9AUB+4Yh2m6Oho9Hp9GTfp3LlzZVwkldjYWK/jDQYDUeXkipjNZsxm/37b8MeHrE7SQQWHqY5YAkrEEijJ4Q3kQmE0K5Wg67tYgtq9eF9pwuFKe7wCgaByas1CMJlMdOvWjVWrVnlsX7VqFX369PG6T+/evcuM//HHH+nevbvX/CWBQCAQCASCmqBWYy6TJ0/mww8/5F//+he///47Tz31FCdPnmT8+PEATJ8+ndGjR2vjx48fz4kTJ5g8eTK///47//rXv1iwYAH/93//V1sPQSAQCAQCwRVArTbfHTFiBBkZGbz44oukpaWRnJzMihUraN5cKe6YlpbmUZOpRYsWrFixgqeeeop//vOfxMfH88477zB06NDaeggCgUAgEAiuAGq1DlNtUNtJYwKBQCAQCKpPbV+/6/8yKIFAIBAIBAIfIwSTQCAQCAQCQSUIwSQQCAQCgUBQCUIwCQQCgUAgEFSCEEwCgUAgEAgElSAEk0AgEAgEAkElCMEkEAgEAoFAUAlCMAkEAoFAIBBUghBMAoFAIBAIBJVQq61RagO1sHlubm4tz0QgEAgEAkFVUa/btdWg5IoTTHl5eQA0bdq0lmciEAgEAoGguuTl5REWFub3415xveRcLhdnzpwhJCQESZJqezq1Sm5uLk2bNuXUqVOir14NIc6pbxDn1TeI8+obxHmtedRzun//ftq0aYNO5/+MoivOYdLpdDRp0qS2p1GnCA0NFW/qGkacU98gzqtvEOfVN4jzWvMkJCTUilgCkfQtEAgEAoFAUClCMAkEAoFAIBBUghBMVzBms5nnn38es9lc21NpMIhz6hvEefUN4rz6BnFea566cE6vuKRvgUAgEAgEguoiHCaBQCAQCASCShCCSSAQCAQCgaAShGASCAQCgUAgqAQhmAQCgUAgEAgqQQimBsQrr7yCJElMmjRJ2ybLMjNmzCA+Pp6AgAD69+/Pvn37PPaz2Ww8+eSTREdHExQUxB133MHp06c9xmRlZTFq1CjCwsIICwtj1KhRZGdn++FR1Q6pqancf//9REVFERgYyNVXX82OHTu028V5rR4Oh4O//vWvtGjRgoCAAK666ipefPFFXC6XNkac08rZsGEDt99+O/Hx8UiSxLJlyzxu9+c5PHnyJLfffjtBQUFER0czYcIEiouLffGwfU5F59VutzN16lQ6duxIUFAQ8fHxjB49mjNnznjchzivZans9VqaRx55BEmSeOuttzy216nzKgsaBNu2bZMTExPlTp06yRMnTtS2v/rqq3JISIi8ZMkSee/evfKIESPkuLg4OTc3Vxszfvx4OSEhQV61apW8c+dOecCAAXLnzp1lh8Ohjbnlllvk5ORkefPmzfLmzZvl5ORk+U9/+pM/H6LfyMzMlJs3by4/8MAD8s8//ywfO3ZMXr16tXz48GFtjDiv1WPmzJlyVFSU/N1338nHjh2Tv/rqKzk4OFh+6623tDHinFbOihUr5GeffVZesmSJDMhff/21x+3+OocOh0NOTk6WBwwYIO/cuVNetWqVHB8fLz/xxBM+Pwe+oKLzmp2dLd90003y4sWL5QMHDshbtmyRr7nmGrlbt24e9yHOa1kqe72qfP3113Lnzp3l+Ph4efbs2R631aXzKgRTAyAvL09u1aqVvGrVKrlfv36aYHK5XHJsbKz86quvamOtVqscFhYmz5s3T5Zl5cPAaDTKX3zxhTYmNTVV1ul08sqVK2VZluX9+/fLgLx161ZtzJYtW2RAPnDggB8eoX+ZOnWqfN1115V7uziv1ee2226TH3zwQY9tQ4YMke+//35ZlsU5vRQuvgD58xyuWLFC1ul0cmpqqjbm888/l81ms5yTk+OTx+svKrqwq2zbtk0G5BMnTsiyLM5rVSjvvJ4+fVpOSEiQf/vtN7l58+YegqmunVcRkmsAPP7449x2223cdNNNHtuPHTtGeno6N998s7bNbDbTr18/Nm/eDMCOHTuw2+0eY+Lj40lOTtbGbNmyhbCwMK655hptTK9evQgLC9PGNCSWL19O9+7dGTZsGDExMXTp0oUPPvhAu12c1+pz3XXXsWbNGg4ePAjAnj17+Omnnxg0aBAgzmlN4M9zuGXLFpKTk4mPj9fGpKSkYLPZPELXDZWcnBwkSSI8PBwQ5/VScblcjBo1iilTptChQ4cyt9e183rFNd9taHzxxRfs3LmTX375pcxt6enpADRu3Nhje+PGjTlx4oQ2xmQyERERUWaMun96ejoxMTFl7j8mJkYb05A4evQo7733HpMnT+aZZ55h27ZtTJgwAbPZzOjRo8V5vQSmTp1KTk4Obdu2Ra/X43Q6efnll7n33nsB8VqtCfx5DtPT08scJyIiApPJ1ODPs9VqZdq0adx3331aY11xXi+N1157DYPBwIQJE7zeXtfOqxBM9ZhTp04xceJEfvzxRywWS7njJEny+FuW5TLbLubiMd7GV+V+6iMul4vu3bvz97//HYAuXbqwb98+3nvvPUaPHq2NE+e16ixevJhPP/2Uf//733To0IHdu3czadIk4uPjGTNmjDZOnNPLx1/n8Eo8z3a7nXvuuQeXy8XcuXMrHS/Oa/ns2LGDt99+m507d1b7sdXWeRUhuXrMjh07OHfuHN26dcNgMGAwGFi/fj3vvPMOBoNBU9QXK+hz585pt8XGxlJcXExWVlaFY86ePVvm+OfPny+j2hsCcXFxtG/f3mNbu3btOHnyJKCcDxDntTpMmTKFadOmcc8999CxY0dGjRrFU089xSuvvAKIc1oT+PMcxsbGljlOVlYWdru9wZ5nu93O8OHDOXbsGKtWrdLcJRDn9VLYuHEj586do1mzZtr168SJE/zlL38hMTERqHvnVQimesyNN97I3r172b17t/bTvXt3Ro4cye7du7nqqquIjY1l1apV2j7FxcWsX7+ePn36ANCtWzeMRqPHmLS0NH777TdtTO/evcnJyWHbtm3amJ9//pmcnBxtTEPi2muv5Y8//vDYdvDgQZo3bw5AixYtxHmtJoWFheh0nh83er1eKysgzunl489z2Lt3b3777TfS0tK0MT/++CNms5lu3br59HHWBqpYOnToEKtXryYqKsrjdnFeq8+oUaP49ddfPa5f8fHxTJkyhR9++AGog+e1yunhgnpB6VVysqwsMw4LC5OXLl0q7927V7733nu9LjNu0qSJvHr1annnzp3yDTfc4HXZZqdOneQtW7bIW7ZskTt27NhglmpfzLZt22SDwSC//PLL8qFDh+TPPvtMDgwMlD/99FNtjDiv1WPMmDFyQkKCVlZg6dKlcnR0tPz0009rY8Q5rZy8vDx5165d8q5du2RAfvPNN+Vdu3Zpq7X8dQ7VZdo33nijvHPnTnn16tVykyZN6u3y94rOq91ul++44w65SZMm8u7du+W0tDTtx2azafchzmtZKnu9XszFq+RkuW6dVyGYGhgXCyaXyyU///zzcmxsrGw2m+Xrr79e3rt3r8c+RUVF8hNPPCFHRkbKAQEB8p/+9Cf55MmTHmMyMjLkkSNHyiEhIXJISIg8cuRIOSsryw+PqHb49ttv5eTkZNlsNstt27aV33//fY/bxXmtHrm5ufLEiRPlZs2ayRaLRb7qqqvkZ5991uOCI85p5axdu1YGyvyMGTNGlmX/nsMTJ07It912mxwQECBHRkbKTzzxhGy1Wn358H1GRef12LFjXm8D5LVr12r3Ic5rWSp7vV6MN8FUl86rJMuyXHU/SiAQCAQCgeDKQ+QwCQQCgUAgEFSCEEwCgUAgEAgElSAEk0AgEAgEAkElCMEkEAgEAoFAUAlCMAkEAoFAIBBUghBMAoFAIBAIBJUgBJNAIBAIBAJBJQjBJBAIGgQzZszg6quvru1paEiSxLJly2p7GgKBoIYQgkkgEFSLefPmERISgsPh0Lbl5+djNBrp27evx9iNGzciSRIHDx709zT9Rl0TagKBwDcIwSQQCKrFgAEDyM/PZ/v27dq2jRs3Ehsbyy+//EJhYaG2fd26dcTHx9O6devamKpAIBDUGEIwCQSCatGmTRvi4+NZt26dtm3dunUMHjyYpKQkNm/e7LF9wIABfPrpp3Tv3p2QkBBiY2O57777OHfuHAAul4smTZowb948j+Ps3LkTSZI4evQoADk5OTz88MPExMQQGhrKDTfcwJ49eyqc68KFC2nXrh0Wy/+3d38hTbVxHMC/p8xM04IyE105tSVFjSQKDdxOpVYSasFm7aayYBEW/YNAo2QZBuJN9FcvJtQKiogutC5qioihrVwU2oUpBmklDdJRafZ7L8JDy/dt6cVbvu/3A4M9/37Pc87Vj/M82wlDSkoKzp8/r7V1d3dDURTcunULqqoiPDwcRqMRzc3NATGqqqqg0+kQHh6O/Px8VFZWYvbs2QAAp9OJ0tJSeL1eKIoCRVHgdDq1sf39/cjPz0d4eDgWLVqEO3fu/PJ9JqI/CxMmIho3s9kMt9utld1uN8xmM0wmk1Y/NDSE5uZmqKqKoaEhOBwOeL1e3L59G11dXdixYwcAYMqUKSgoKMDVq1cD5nC5XEhLS0NiYiJEBDk5Oejr60NtbS08Hg9SU1Oxbt06vH///m/XWFVVheLiYpSVlaG9vR2nT5/G8ePHUVNTE9CvuLgYR44cQVtbGwwGA7Zt26ZtNzY1NcFut+PAgQNoa2tDZmYmysrKtLFWqxWHDx/G0qVL0dvbi97eXlitVq29tLQUFosFT58+xaZNm2Cz2f5xvUT0hxvXq3qJiETk8uXLEhERIcPDw/LhwwcJCQmRN2/eyPXr1yU9PV1ERBoaGgSAdHZ2jhnf0tIiAGRgYEBERB4/fiyKokh3d7eIiIyMjEhcXJycO3dORETu378vUVFRY94unpSUJJcuXRIRkRMnTojRaNTadDqduFyugP4Oh0PS0tJERLS30FdXV2vtz58/FwDS3t4uIiJWq1VycnICYthsNpk1a5ZW/nHeUQCkpKREKw8ODoqiKFJXVzemLxH9+fiEiYjGTVVV+P1+tLa2orGxEQaDAfPmzYPJZEJrayv8fj/q6+uxYMECJCYm4smTJ8jNzcXChQsRGRkJs9kMAOjp6QEArFixAikpKbh27RoAoKGhAW/fvoXFYgEAeDweDA4OYs6cOZg5c6b26erqQmdn55j1vXv3Dq9evUJhYWFA/1OnTo3pv3z5cu17bGwsAGjbhS9evMCqVasC+v9Y/pnvY0dERCAyMlKLTUSTS8jvXgARTT7JycmIj4+H2+2Gz+eDyWQCAMyfPx96vR5NTU1wu91Yu3Yt/H4/srKykJWVhStXriA6Oho9PT3Izs7G0NCQFtNms8HlcuHYsWNwuVzIzs7G3LlzAXw75xQbGxtwbmrU6Hmi7339+hXAt2251atXB7RNnTo1oDxt2jTtu6IoAeNFRKsbJSK/covGxB6NPxqbiCYXJkxENCGqqqK+vh4+nw9Hjx7V6k0mE+7du4eHDx9i586d6OjoQH9/P8rLy6HT6QAg4Bd2o7Zv346SkhJ4PB7cvHkTFy5c0NpSU1PR19eHkJAQJCQkBF1bTEwM4uLi8PLlS9hstglfY0pKClpaWgLqflx7aGgoRkZGJjwHEU0OTJiIaEJUVcW+ffswPDysPWECviVMe/fuxadPn6CqKsLCwhAaGoqzZ8/Cbrfj2bNncDgcY+Lp9Xqkp6ejsLAQX758QW5urta2fv16pKWlIS8vD2fOnMHixYvx+vVr1NbWIi8vDytXrhwT7+TJk9i/fz+ioqKwceNGfP78GY8ePYLP58OhQ4d+6RqLioqQkZGByspKbN68GQ8ePEBdXV3AU6eEhAR0dXWhra0N8fHxiIyMxPTp08dzK4loEuAZJiKaEFVV8fHjRyQnJyMmJkarN5lMGBgYQFJSEnQ6HaKjo+F0OnHjxg0sWbIE5eXlqKio+NuYNpsNXq8XW7ZswYwZM7R6RVFQW1uLjIwM7Nq1CwaDAQUFBeju7g6Y+3u7d+9GdXU1nE4nli1bBpPJBKfTCb1e/8vXuGbNGly8eBGVlZUwGo24e/cuDh48iLCwMK3P1q1bsWHDBqiqiujoaO0cFhH9tygyng15IqL/uT179qCjowONjY2/eylE9C/ilhwR0U9UVFQgMzMTERERqKurQ01NTcAfYBLR/wOfMBER/YTFYkF9fT0GBgaQmJiIoqIi2O32370sIvqXMWEiIiIiCoKHvomIiIiCYMJEREREFAQTJiIiIqIgmDARERERBcGEiYiIiCgIJkxEREREQTBhIiIiIgqCCRMRERFREEyYiIiIiIL4C10Yctm0w3dQAAAAAElFTkSuQmCC", - "text/plain": [ - "
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VWL16NSZOnIgVK1agc+fO6N27N0aOHIkOHTrc9rUXLlyAEALBwcFVPh8cHIwbN24gPT0dbm5uuHDhAg4dOmQoMsaOHYvJkydj3rx5lfa66ty5M4YPH46ZM2fi999/r/L827ZtQ/v27eHj4wMA+OKLLzBmzBg4OzsjJCQEPXv2xGOPPYYePXrc8TrI5XK0bNnSMMdo2LBh2LlzJ3r37g0PDw9069YN9913Hx5//HHY2dkBAObNm4dHH30U/v7+aNmyJcLDwzFo0CA89thjdbJ3V11rfImIiJoInV5g/4V0TF57DKELovDimmP4+fg15JVo4Wqrxpiuvvj26S6ImdsPS0Z2wsD2nrBWK8snpCoVsNOo4GqrhrejFYJcbdDGyw6dfR3RPcgFix5tjx+eDUdzNxtk5Jdi8tpjeGr1ESRlFd45GN3W0KFDce3aNWzduhUDBgzAnj170LlzZ6xevfquzlvR81NRpK5cuRIDBgyAi4sLAGDQoEEoKCjAb7/9VuXr33rrLezfvx+7du2q8vktW7YYen8AoFevXrh8+TJ+//13DB06FKdPn0ZERAQWLFhQ7bwVWRUKBVatWoWrV6/ivffeg5eXFxYuXIi2bdsiJSUFAODp6Yno6GicPHkSkydPRllZGZ544gncf//9hnlCjYqgSnJycgQAkZOTI3UUIjJBVzLyxeKdZ0X4278Jv9d+MTx6v/eHePvXv8XRK1lCp9PXyfcqLtOKJVHnRYvZ24Tfa7+I1nO3iy/3XRJlWl2dnL+2ioqKxN9//y2KiooMx/R6vSgoKWvwh15/99d6/PjxwtfXVwghhJ+fn/joo48qtcnIyBAymUwsXLiwynNMnDhRODo6Cr1eL7RarfD09BQymUwoFArDA4AYPny44TXz5s0TISEhhq+ff/550alTJxEbGysAiPj4eCGEEKWlpcLOzk4cO3bstu9jwYIFQqVSiZKSEiGEEL179xZTpkyp1E6r1QpHR0fxwgsv3PJcWVlZwsXFRfzf//3fLdvs379fABB//PFHlc9/8803ws7OzujYDz/8IG5XnlT1s1WhJp/fHAIjIqoDBSVabDuZgh9jruJw/D93+thplBjSsRmGhXmjfTP7Oh+iUisVmNK3BQZ38MTsTSdxOD4Lb/16BpvjkrHokQ5o721fp9/vbshksmoPRTU2bdq0webNm2/bxtnZGf369cOyZcswbdo0o3lAqamp+P777/H4449DJpMZ5uYcO3YMCoXC0O7s2bMYM2YMMjMzq9xD7f/+7/8QFBRU6Zb53bt3w8HBAR07drzj+9BqtSguLr7tburffPMNbty4gaFDh96yjaOjIzw9PVFQUHDb7wfglm1cXV2Rl5eHgoICWFtbAyi/xb8hmOZPIhFRI6DTCxy5koUNMVfx68kUFJaW700kkwERLVwxLNQb/dq4Q6NS3OFMd6+5mw3WTeyGH2OSsPDXMziVnIshnx3AxF6BeG1Aa8jljXduUGOSmZmJYcOG4emnn0aHDh1ga2uLo0eP4r333sOQIUMM7ZKTkyt9UPv6+uLTTz9F9+7dMWDAALz11lsICAjA6dOn8corr6BZs2ZYuHAhgPLhr8GDByMkJMToHG3btsXUqVPx3XffYcqUKZXyubu7Y/r06Xj//feNjm/dutVo+AsoX+Nn1KhRCAsLg7OzM/7++2/Mnj0bkZGRhnk7QPmt+KmpqdBqtUhOTsbGjRvx0Ucf4bnnnkNkZCSA8vV94uLi8MgjjyAoKAjFxcX49ttvcfr0aXzyyScAgOeeew5eXl6499574e3tjZSUFLz11ltwdXVFeHg4AGDTpk2YNWsWzp49CwDo2rUrrKysMHv2bLz00ks4fPjwXQ81Vtsd+4jMEIfAiOhWikq14vczqeLVH4+LzvN3VRri+vSPC+JadqGkGdNyi8VLa2INuT75/XyDZ7jdMEVjVlxcLGbOnCk6d+4s7O3thZWVlWjVqpWYO3euKCws///q5+cnAFR6rFq1SgghxJUrV8STTz4pPDw8hEqlEj4+PuKll14SGRkZQgghUlNThVKpFD/88EOVGV566SXRvn17IUTlITAhhMjNzRUuLi5GQ2A+Pj4iKirKqN3bb78twsPDhZOTk9BoNCIwMFBMnjzZkEOI8iGwivwWFhbC09NTPPDAA2Ljxo1G54qNjRVjx44VAQEBQq1WC2dnZ9GrVy+xdetWQ5uffvpJDBo0SHh6egoLCwvh5eUlhg4dKk6cOGFos2rVqkrDW5s2bRLNmzcXGo1GPPDAA+KLL75okCEwmRBcZeu/cnNzYW9vj5ycHKMqmYjMU15xGXafS8fO06nYczYNBaX/7EJtb6nCgLbuGBbmgzA/x0Z1F9Z3hxIwd/MpyGTA10/eg8hWbg32vYuLixEfH4+AgABoNJoG+77mKDY2Fvfeey/S09OhUqmkjlPvbvezVZPPbw6BERFVIS2vGL+fScPO06k4eDETpbp/7mLxsNOgf1t3DGjrgS4BTlApGucNtWO7+eH0tVysPZyIKWuP4eeXesLP2VrqWFTHtFqtYVVmqj4WQEREALQ6PY4lZWPPuTTsOZeO09dyjZ4PdLXGgLYeGNDWAx2a2ZvMnJo3HmqDMym5iEvKxrP/i8HG57ub7ERkqlqXLl2qvVI1/YN/C4jIbF3PLcbec+nYcz4N+y9kIK9Ya/R8iLc9+t8sepq72UiU8u6olQosH9sZD35yAGdT8zBr40ksGdGxUQ3VEUmBBRARmZVzqXnYdCwZe86l4Wyq8RYBjlYq9Grpit4tXdGrpStcbNQSpaxbnvaW+Gx0Z4z56i9sibuGDt4OGN8zQOpYRJJiAUREZiEm4QaW77mI386kGY7JZEAHbwf0aemKPq1c0cHbAQoTGdqqqa6BzpgzOBhv/vw33t52Bm297NCtATZS5X02VNfq6meKBRARNVlCCOy/kIFley7i0OXyxQllMqB/G3cMbOeJiBYucG4ivTzV8WR3fxxPysbmuGt4cU0sfn6pJzztLe/8wlqomJBbWFhYaWNQortRWloKAEYLSNYGCyAianL0eoGdp1OxbM8lnEzOAQCoFDI80qkZnu0dhCBX05zPc7dkMhkWPdoB567n40xKLiZ9F4sfnu0GtbLuF2pUKBRwcHBAWlp5j5uVlRXnHdFd0+v1SE9Ph5WVFZTKuythWAARUZNRqtVjc1wyVuy9hMvp5UvvW6oUGNXFFxMiAuDlwJ4ISwsFPh8bigc/PYDjSdl4Y+tpLHr09ruc15aHhwcAGIogorogl8vh6+t71wU1CyAiahL2nk/HrA0ncC2nGED5HlxPdvfHkz0C4GR96z2PzJGvsxWWjuqEJ1cdxtrDSejg7YBRXXzr/PvIZDJ4enrCzc0NZWVldX5+Mk8WFhaQy+9+7S0WQERk8tYfScTsTaeg0wu42aoxISIAo7v6wUbNX3G30rulK2b0b4X3d57DvC2nEexph44+DvXyvRQKxV3P1yCqa41z+VIiomoQQuCjqPN4bcNJ6PQCj3Zuhn2vRuKZXkEsfqrh+T5BGNDWHaU6PV74PhbFZbo7v4ioiWABREQmqUynx2sbTuDj3y8AAF66tzk+GBbSIDuvNxUymQyLh4XA016D5OwifHPwitSRiBoMCyAiMjkFJVpM+OYofjh6FXIZsPCRdni5fyveZVQLthoVpvdrCQD4bPdFZBeWSpyIqGGwACIik5KeV4KRXxzC3vPp0Kjk+GJcGMZ09ZM6lkl7tLM3WnvYIrdYi892X5Q6DlGDkLwAWrZsmWFL+9DQUOzfv/+WbVNSUjB69Gi0atUKcrkcU6dOrbLdhg0b0KZNG6jVarRp0wabNm2qp/RE1JAupefj0eV/4mRyDpytLbDumXD0beMudSyTp5DLMHNgawDANwcTkJRVKHEiovonaQG0fv16TJ06FXPmzMGxY8cQERGBgQMHIjExscr2JSUlcHV1xZw5cxASElJlm+joaIwYMQLjxo3D8ePHMW7cOAwfPhx//fVXfb4VIqpnMQlZGLr8IJKyiuDvbIUNz3Wvt7uWzFHvlq7o0dwZpTo9Pow6L3UcononExJu1NK1a1d07twZy5cvNxwLDg7Gww8/jEWLFt32tX369EHHjh2xZMkSo+MjRoxAbm4utm/fbjh2//33w9HREWvXrq1WrtzcXNjb2yMnJwd2dnbVf0NEVC92nErFlHXHUKLVI8THAV8/EWZWW1g0lJNXc/DgpwcAAL+81BPtmtlLnIioZmry+S1ZD1BpaSliYmLQv39/o+P9+/fHwYMHa33e6OjoSuccMGDAbc9ZUlKC3NxcowcRNQ67Tqfiue9jUKLVo2+wG9ZN7Mbip56097bHkI5eAIB3tp+VOA1R/ZKsAMrIyIBOp4O7u/H4vbu7O1JTU2t93tTU1Bqfc9GiRbC3tzc8fHx8av39iajuJGUV4uUfj0MIYFioN1aMDYWlBW9zr08z+reChUKOAxczsO98utRxiOqN5JOg/3vbqhDirm9lrek5Z82ahZycHMMjKSnprr4/Ed29Eq0OL6yJRV6xFp18HfD2o+2hVEj+K6vJ83Gywrjw8rvqFm0/C51eslkSRPVKst8mLi4uUCgUlXpm0tLSKvXg1ISHh0eNz6lWq2FnZ2f0ICJpLdp2Fieu5sDBSoVPR3eGisVPg3kxsjlsNUqcScnF5mPJUschqheS/UaxsLBAaGgooqKijI5HRUWhe/futT5veHh4pXPu2rXrrs5JRA1r+8kUrL65KvGHw0PQjLu4NyhHawu8ENkcAPDBrnPcIoOaJEk3y5k+fTrGjRuHsLAwhIeH44svvkBiYiImTZoEoHxoKjk5Gd9++63hNXFxcQCA/Px8pKenIy4uDhYWFmjTpg0AYMqUKejVqxfeffddDBkyBFu2bMFvv/2GAwcONPj7I6KaS8gswKs/nQAAPNs7EPe25jo/Uniyuz++PXgF13KK8c3BK3i2d5DUkYjqlKQF0IgRI5CZmYn58+cjJSUF7dq1w7Zt2+DnVz7+nJKSUmlNoE6dOhn+HBMTgzVr1sDPzw9XrlwBAHTv3h3r1q3D3Llz8frrryMoKAjr169H165dG+x9EVHtFJfdnPdTokWYnyNm9G8ldSSzpVEpML1/K8z48Tg+3X0Rw8N84GhtIXUsojoj6TpAjRXXASKSxuubT+F/hxLgaKXCtikR8LTn0JeUdHqBwUv342xqHib0DMDcB9pIHYnotkxiHSAion/7+fg1/O9QAgDgwxEdWfw0Agq5DLMGBQMAvo3mFhnUtLAAIiLJxWcUYNbGkwCA5/sEIbKVm8SJqEKvFi6GLTI+2HVO6jhEdYYFEBFJqrhMh+e/j0V+iRZdApwwvV9LqSPRv8hkMswaWN4LtDnuGk4l50iciKhusAAiIkm9+fPfOJOSC2drC3wyqhMXO2yE2jWzx8M3t8jgRqnUVPA3DRFJZktcMtYeToRMBiwZ2RHudhqpI9EtTOlb3jO3+1wart7gXCAyfSyAiEgSl9LzMfvmvJ+XIpsjooWrxInodgJcrNGjuTOEANYf4XZBZPpYABFRgysq1eGF72NRUKpDt0AnQ+8CNW6juvgCKC+AtDq9xGmI7g4LICJqcG9sPY2zqXlwsVFj6chOUMjvbgNkahj923jA2doCaXkl+P1smtRxiO4KCyAialAbY69i/dEkyGTAxyM7wo3zfkyGhVKOx8K8AQBrDyfeoTVR48YCiIgazIXreZiz6RQAYMp9LdCjuYvEiaimRt1TPgy293w6F0Ykk8YCiIgaRGGpFs9/H4uiMh16NnfBS/e2kDoS1YK/izV6NneBEMAPRzkZmkwXCyAiahCvbz6NC2n5cLNVY8nIjpz3Y8L+PRm6jJOhyUSxACKievfD0SRsiL0KuQxYOqoTXGzUUkeiu9CvjTtcbG5Ohj7DydBkmlgAEVG9Opeah//bUj7v5+X+rdAt0FniRHS3LJRyPBbqA4CTocl0sQAionpTUKLF89/HoLhMj94tXfFc7yCpI1EdGdWlvADad4GTock0sQAionohhMCcTSdxKb0AHnYafDSiI+Sc99Nk+Dn/MxmaK0OTKWIBRET1Yt2RJGyOuwaFXIZPR3eCk7WF1JGojo3uenMy9FFOhibTwwKIiOrc39dyMW/raQDAKwNaIczfSeJEVB/KJ0Orkc7J0GSCWAARUZ1KzyvBc9/HoFSrx72t3fBMRKDUkaieqBRyDLu5MvQaToYmE8MCiIjqTE5RGR7/+jASMgvh7WiJD4aFcN5PE1exMvR+ToYmE8MCiIjqRFGpDuNXH8GZlFy42Kjx3fiucOS8nybP19kKES3KJ0OvO8JeIDIdLICI6K6VavWY9F0MjibcgJ1Gif+N7wJ/F2upY1EDGX1zZegfjl7lZGgyGSyAiOiu6PQC03+Iw97z6dCo5Fj11D0I9rSTOhY1oL5Gk6GvSx2HqFpYABFRrQkh8PqWU/jlRApUChk+HxeGUD/e8WVuVAo5ht+cDP39XxwGI9PAAoiIau39neew5q9EyGTAkhGd0Lulq9SRSCIVG6Tuv5DBydBkElgAEVGtfL73EpbtuQQAePuR9hjcwVPiRCQlH6fyydAA9wcj08ACiIhqbO3hRCzafhYAMHNga8O//sm8jenKydBkOlgAEVGN/HoiBbM3nQQAPNcnCJO4wSnddF+wO1xt1cjIL0HU35wMTY0bCyAiqrZ959Mxdf0xCFE+5+PVAa2kjkSNiEohx2Oh5ZOht8ZdkzgN0e2xACKiaolLysak72JQphN4oIMn3nq4HWQyrvJMxga3L58Ltvd8OorLdBKnIbo1FkBEdEeX0/Px9OojKCzVIaKFCz4c3hEKbnFBVWjrZYdmDpYoKtNh3/l0qeMQ3RILICK6reu5xRi38jCyCkrRwdsey8eGwkLJXx1UNZlMhv5t3QEAO09zHhA1XvwtRkS3lFtchie+Pozk7CL4O1vh6yfvgY1aKXUsauQGtPUAAPx+9jq0vBuMGikWQERUpeIyHSZ+cxRnU/PgaqvG/8Z3hYuNWupYZALu8XeCk7UFsgvLcDg+S+o4RFViAURElej0AlPXxeGv+CzYqpVY/dQ98HGykjoWmQiFXIa+wW4AgJ2nUyVOQ1Q1FkBEZEQIgf/bcgo7TqfCQiHHF4+Hoa2XvdSxyMRUDIPt+vs6hBASpyGqjAUQERlZ+vtFfF+xv9fIjggPcpY6EpmgHs1dYG2hQEpOMU5czZE6DlElLICIyGDNX4n46LfzAID5D7XFoPbc34tqR6NSoE8rDoNR48UCiIgAADtOpWLu5vItLibf2xzjwv2lDUQm75/b4VkAUePDAoiIEJOQhcnrjkEvgFFdfDCtX0upI1ETENnaDSqFDJfSC3AxLV/qOERGWAARmbmcojK8tOYYSrV69A12x4Ih3OKC6oadRoXuQS4A2AtEjQ8LICIz939bTuFaTjH8nK3w8ciOUCr4a4HqjuFuMBZA1MjwNx2RGdsSl4wtcdegkMvw0YiOsOYqz1TH+rVxh0wGHL+ag5ScIqnjEBmwACIyU8nZRZi7+RQA4KV7m6Ozr6PEiagpcrVVI/Tmz9Yu7g1GjQgLICIzpNMLTF8fh7xiLTr6OODFyOZSR6ImrGIYjPOAqDFhAURkhr7afxl/xWfBykKBJSM474fqV0UB9Fd8Fm4UlEqchqgcf+sRmZnT13KweNc5AMC8B9vA38Va4kTU1Pk6W6G1hy10eoHfz6ZJHYcIAAsgIrNSXKbD1HVxKNMJ9G/jjuFhPlJHIjPBYTBqbFgAEZmRd7afxYW0fLjaqvHO0A5c74caTEUBtO98OgpLtRKnIWIBRGQ29p5Px+qDVwAA7z/WAU7WFtIGIrMS7GkLHydLlGj12Hc+Xeo4RCyAiMxBVkEpZvx4HADwRLifYZNKooYik8kwoE3FMBhvhyfpSV4ALVu2DAEBAdBoNAgNDcX+/ftv237v3r0IDQ2FRqNBYGAgVqxYUanNkiVL0KpVK1haWsLHxwfTpk1DcXFxfb0FokZNCIGZG04gPa8Ezd1sMGtQsNSRyEwNaFdeAP1+5jpKtXqJ05C5k7QAWr9+PaZOnYo5c+bg2LFjiIiIwMCBA5GYmFhl+/j4eAwaNAgRERE4duwYZs+ejcmTJ2PDhg2GNt9//z1mzpyJefPm4cyZM1i5ciXWr1+PWbNmNdTbImpUfjx6Fbv+vg6VQoYlIzpCo1JIHYnMVGdfR7jYWCC3WItDlzOljkNmTtIC6MMPP8T48eMxYcIEBAcHY8mSJfDx8cHy5curbL9ixQr4+vpiyZIlCA4OxoQJE/D0009j8eLFhjbR0dHo0aMHRo8eDX9/f/Tv3x+jRo3C0aNHG+ptETUaCZkFeOPn0wCAl/u3Qrtm9hInInOmkMvQr407AN4NRtKTrAAqLS1FTEwM+vfvb3S8f//+OHjwYJWviY6OrtR+wIABOHr0KMrKygAAPXv2RExMDA4fPgwAuHz5MrZt24bBgwffMktJSQlyc3ONHkSmTq8XeOXHEygs1aFLgBMmRgRKHYkI/W/eDRb193Xo9ULiNGTOJCuAMjIyoNPp4O7ubnTc3d0dqalV/8sgNTW1yvZarRYZGRkAgJEjR2LBggXo2bMnVCoVgoKCEBkZiZkzZ94yy6JFi2Bvb294+PhwbRQyfasOXsHhK1mwtlDgg2EhUMh5yztJr3uQM2zUSqTlleBYUrbUcciMST4J+r/rkAghbrs2SVXt/318z549WLhwIZYtW4bY2Fhs3LgRv/zyCxYsWHDLc86aNQs5OTmGR1JSUm3fDlGjcDk9H+/tOAsAmD04GD5OVhInIiqnVioQ2br8LsRdHAYjCSml+sYuLi5QKBSVenvS0tIq9fJU8PDwqLK9UqmEs7MzAOD111/HuHHjMGHCBABA+/btUVBQgGeeeQZz5syBXF655lOr1VCr1XXxtogkp9MLzPjxOEq0ekS0cMHoLr5SRyIyMqCtO34+fg07T6di5sDWXJCTJCFZD5CFhQVCQ0MRFRVldDwqKgrdu3ev8jXh4eGV2u/atQthYWFQqVQAgMLCwkpFjkKhgBDC0FtE1JR9tf8yYhOzYatW4l2u9kyNUJ9WbrBQynElsxDnr+dLHYfMlKRDYNOnT8dXX32Fr7/+GmfOnMG0adOQmJiISZMmASgfmnr88ccN7SdNmoSEhARMnz4dZ86cwddff42VK1dixowZhjYPPvggli9fjnXr1iE+Ph5RUVF4/fXX8dBDD0Gh4O2/1LRduJ6HD6LOAwBef6ANvBwsJU5EVJmNWokeQeW99rvPcXNUkoZkQ2AAMGLECGRmZmL+/PlISUlBu3btsG3bNvj5+QEAUlJSjNYECggIwLZt2zBt2jR89tln8PLywtKlSzF06FBDm7lz50Imk2Hu3LlITk6Gq6srHnzwQSxcuLDB3x9RQ9Lq9Hj5x+Mo1eoR2coVw8K8pY5EdEu9W7pi97l07Dufjkm9g6SOQ2ZIJjguVElubi7s7e2Rk5MDOzs7qeMQVctnuy/i/Z3nYKdRYte03vCw10gdieiWLqXn474P9sJCIUfcvH6wspD03+PURNTk81vyu8CI6O6dScnFkt/Kh77eeKgtix9q9AJdrNHMwRKlOj3+upwldRwyQyyAiExcmU6Pl384jjKdQL827nikUzOpIxHdkUwmQ6+WLgCAfRe4Ozw1PBZARCbu0z8u4u+UXDhYqbDwkXa864tMRq8WrgCAfedZAFHDYwFEZMJOJefgs90XAQALhrSDmy2Hvsh0dA9ygVwGXEovQHJ2kdRxyMywACIyUSVaHV7+4Ti0eoFB7T3wQAdPqSMR1Yi9lQodfRwAAAc4DEYNjAUQkYla+vsFnLueB2drCywYwqEvMk0RhmGwDImTkLlhAURkgs6k5GL5nksAgLcebgdnG27lQqapV8vyAujAxQzouDs8NSAWQEQmRgiBt379G3oBDGzngYHtOfRFpivE2x62GiVyispw4mq21HHIjLAAIjIxf5xNw58XM2GhkGP2oGCp4xDdFaVCjp7Ny2+H33+Bw2DUcFgAEZmQMp0eC7edAQA83TMAPk5WEiciunsRvB2eJMACiMiEfHcoAZfTC+BsbYEXIrl/EjUNES3Ke4COJWUjt7hM4jRkLlgAEZmI7MJSLPntAgBgev+WsNWoJE5EVDd8nKwQ6GINnV7g4MVMqeOQmWABRGQilv5+ETlFZWjlbosRYT5SxyGqUxW9QPu5HhA1EBZARCbgcno+vo2+AgCYMzgYSgX/6lLTUnE7/L4L6RCCt8NT/eNvUSIT8Pa2s9DqBSJbuRo+KIiakm6BzlApZEjKKkJCZqHUccgMsAAiauQOXszAb2euQyGXYc5g3vZOTZO1WolQP0cAHAajhsECiKgR0+kFFvxaftv72K6+aO5mK3EiovpTcTv8Xm6LQQ2ABRBRI/ZTTBLOpOTCVqPElL4tpY5DVK963xzejb6UgTKdXuI01NSxACJqpPJLtHh/53kAwJT7WsDJ2kLiRET1q42nHZytLVBQqkNswg2p41ATxwKIqJFasecSMvJL4O9shcfD/aWOQ1Tv5HIZerbgthjUMFgAETVCydlF+HL/ZQDArEHBsFDyryqZB8O2GJwITfWMv1WJGqF3t59FiVaPrgFO6N/GXeo4RA2m180eoJPJOcgqKJU4DTVlLICIGpnYxBvYevwaZDLg9QfaQCaTSR2JqMG42WnQ2sMWQgAHLnIYjOoPCyCiRkQIgYU3b3t/rLM32jWzlzgRUcOrWOxzP3eHp3rEAoioEdl/IQMxCTegUckxY0ArqeMQSaJiXzBui0H1iQUQUSPy2e6LAIBRXXzhbqeROA2RNO7xd4JaKcf13BJcSMuXOg41USyAiBqJo1ey8Fd8FlQKGZ7pFSh1HCLJaFQKdA10BgDs4zAY1RMWQESNxKc3e38eC/WGp72lxGmIpNXLMAzGidBUP1gAETUCp5JzsOdcOuQy4NleQVLHIZJcxUTovy5norhMJ3EaaopYABE1AhVzfx4M8YK/i7XEaYik18LNBh52GpRo9ThyJUvqONQEsQAiktjFtDzsOJ0KAHi+T3OJ0xA1DjKZzHA3GLfFoPrAAohIYsv2XIIQQP827mjlYSt1HKJGI+LmMBgnQlN9YAFEJKGkrEJsibsGAHghkr0/RP/Ws7kLZDLgbGoe0nKLpY5DTQwLICIJrdh7CTq9QEQLF4T4OEgdh6hRcbK2QDuv8tXQoy9nSpyGmhoWQEQSuZ5bjB+PXgXA3h+iW+keVL4e0MGLLICobrEAIpLIV/svo1SnR5ifI7oGOEkdh6hR6lZRAF3mRGiqWyyAiCRwo6AU3/+VCKC894c7vhNV7R5/JyjlMiRlFSEpq1DqONSEsAAiksCqP+NRWKpDWy879GnlKnUcokbLRq00zI+LvsRhMKo7LICIGlhecRlWH7wCgL0/RNVhmAd0icNgVHdYABE1sP8dSkBusRZBrta4v62H1HGIGr3wmwVQ9OVMCCEkTkNNBQsgogZUVKrDyv3xAIDn+jSHXM7eH6I76ezrCAulHNdzS3A5o0DqONREsAAiakDrjyQis6AU3o6WGNLRS+o4RCZBo1IgzM8RAHCQ84CojrAAImogpVo9Pt93GQDwbO8gqBT860dUXRXzgKI5D4jqSK1/A+/fvx9jx45FeHg4kpOTAQD/+9//cODAgToLR9SUbDp2FSk5xXC1VWNYqLfUcYhMimEe0KVM6PWcB0R3r1YF0IYNGzBgwABYWlri2LFjKCkpAQDk5eXh7bffrtOARE2BXi8MvT8TIwKgUSkkTkRkWjp4O8DKQoEbhWU4m5ondRxqAmpVAL311ltYsWIFvvzyS6hUKsPx7t27IzY2ts7CETUVey+k43J6AWzUSozq4it1HCKTo1LI0eXmium8HZ7qQq0KoHPnzqFXr16VjtvZ2SE7O/tuMxE1Oav+vAIAGBbmDVuN6vaNiahK3f81DEZ0t2pVAHl6euLixYuVjh84cACBgYF3HYqoKbmYlo9959MhkwFPdveXOg6Ryeoe5AIA+Cs+C1qdXuI0ZOpqVQA9++yzmDJlCv766y/IZDJcu3YN33//PWbMmIHnn3++rjMSmbTVB8vX/bmvtRv8nK0lTkNkuoI97WBvqUJ+iRanruVKHYdMnLI2L3r11VeRk5ODyMhIFBcXo1evXlCr1ZgxYwZefPHFus5IZLJyCsuwIab8LsmnegRInIbItCnkMnQLdMLO09dx8FIGOt7cI4yoNmp9G/zChQuRkZGBw4cP49ChQ0hPT8eCBQvqMhuRyVt/NBFFZTq0crc1zF8gotqrGAbjPCC6W3e1EpuVlRXCwsLQunVr/Pbbbzhz5kyNz7Fs2TIEBARAo9EgNDQU+/fvv237vXv3IjQ0FBqNBoGBgVixYkWlNtnZ2XjhhRfg6ekJjUaD4OBgbNu2rcbZiO6GVqfHNwcTAABP9vDnpqdEdaDiHxJHrmShRKuTOA2ZsloVQMOHD8enn34KACgqKsI999yD4cOHo0OHDtiwYUO1z7N+/XpMnToVc+bMwbFjxxAREYGBAwciMTGxyvbx8fEYNGgQIiIicOzYMcyePRuTJ082+p6lpaXo168frly5gp9++gnnzp3Dl19+iWbNmtXmrRLV2m9n0pCcXQQHKxUe7sifP6K60NzNBi42ahSX6RGXmC11HDJhtSqA9u3bh4iICADApk2boNfrkZ2djaVLl+Ktt96q9nk+/PBDjB8/HhMmTEBwcDCWLFkCHx8fLF++vMr2K1asgK+vL5YsWYLg4GBMmDABTz/9NBYvXmxo8/XXXyMrKwubN29Gjx494Ofnh549eyIkJKQ2b5Wo1lb9WT75eVQXX1hacOFDorogk8kMq0JzXzC6G7UqgHJycuDkVL4g1Y4dOzB06FBYWVlh8ODBuHDhQrXOUVpaipiYGPTv39/oeP/+/XHw4MEqXxMdHV2p/YABA3D06FGUlZUBALZu3Yrw8HC88MILcHd3R7t27fD2229Dp7t1V2lJSQlyc3ONHkR34/S1HPwVnwWFXIbHw/2kjkPUpHA9IKoLtSqAfHx8EB0djYKCAuzYscNQlNy4cQMajaZa58jIyIBOp4O7u7vRcXd3d6Smplb5mtTU1Crba7VaZGSUrwx6+fJl/PTTT9DpdNi2bRvmzp2LDz74AAsXLrxllkWLFsHe3t7w8PHxqdZ7ILqV1TcXPhzYzgOe9pbShiFqYioKoGNJN1BYqpU4DZmqWhVAU6dOxZgxY+Dt7Q0vLy/06dMHQPnQWPv27Wt0rv9ODBVC3HayaFXt/31cr9fDzc0NX3zxBUJDQzFy5EjMmTPnlsNqADBr1izk5OQYHklJSTV6D0T/lplfgi3HrwHgre9E9cHXyQrNHCxRphM4euWG1HHIRNVqHaDnn38eXbp0QVJSEvr16we5vLyOCgwMrPYcIBcXFygUikq9PWlpaZV6eSp4eHhU2V6pVMLZufxfBJ6enlCpVFAo/plzERwcjNTUVJSWlsLCwqLSedVqNdRqdbVyE93Jmr8SUarVI8TbHp19HaSOQ9TkVMwD+inmKqIvZ6JXS1epI5EJqvVt8GFhYXjkkUdgY2NjODZ48GD06NGjWq+3sLBAaGgooqKijI5HRUWhe/fuVb4mPDy8Uvtdu3YhLCzMsClrjx49cPHiRej1/yyTfv78eXh6elZZ/BDVpVKtHv87VH7r+1M9AnjrO1E96c6J0HSXatUDpNPpsHr1avz+++9IS0szKjYA4I8//qjWeaZPn45x48YhLCwM4eHh+OKLL5CYmIhJkyYBKB+aSk5OxrfffgsAmDRpEj799FNMnz4dEydORHR0NFauXIm1a9cazvncc8/hk08+wZQpU/DSSy/hwoULePvttzF58uTavFWiGtl+KgVpeSVws1VjUHtPqeMQNVkVd4KdvJqN3OIy2HGTYaqhWhVAU6ZMwerVqzF48GC0a9eu1v/KHTFiBDIzMzF//nykpKSgXbt22LZtG/z8yu+aSUlJMVoTKCAgANu2bcO0adPw2WefwcvLC0uXLsXQoUMNbXx8fLBr1y5MmzYNHTp0QLNmzTBlyhS89tprtcpIVBNf35z8PLabHyyUd7XOKBHdhqe9JQJcrBGfUYDDl7PQt03VUyeIbkUmKmYR14CLiwu+/fZbDBo0qD4ySS43Nxf29vbIycmBnZ2d1HHIRMQm3sCjyw7CQiHHwVn3wsWG88qI6tPsTSex5q9EPN0jAP/3YBup41AjUJPP71r9E9XCwgLNmzevVTiipmrVzd6fhzp6sfghagD/zAPKkDgJmaJaFUAvv/wyPv74Y9Si84ioSUrNKcb2kykAgKd6+EsbhshMdAssL4DOpuYhM79E4jRkamo1B+jAgQPYvXs3tm/fjrZt2xruwKqwcePGOglHZCr+d+gKtHqBLgFOaOtlL3UcIrPgYqNGaw9bnE3Nw6HLWRjcgTceUPXVqgBycHDAI488UtdZiExScZkOa/4qn6z/NHt/iBpUeJAzzqbm4eClDBZAVCO1KoBWrVpV1zmITNaWuGTcKCxDMwdL9GvjIXUcIrPSPcgFq/68gujLXA+IaqZWBVCF9PR0nDt3DjKZDC1btoSrK1fjJPMihMA3B8sXPnw83A8KORc+JGpIXQKcIJcBl9MLkJpTDA/76u1HSVSrSdAFBQV4+umn4enpiV69eiEiIgJeXl4YP348CgsL6zojUaMVm3gDf6fkQq2UY8Q93ESXqKHZW6rQrln5vLvoy7wbjKqvVgXQ9OnTsXfvXvz888/Izs5GdnY2tmzZgr179+Lll1+u64xEjdb/ost7fx4K8YKDFbdaIZJCxarQBy9yGIyqr1YF0IYNG7By5UoMHDgQdnZ2sLOzw6BBg/Dll1/ip59+quuMRI1SRn4Jtp0s35x3XLifxGmIzFf3IBcA3BeMaqZWBVBhYWGVO7a7ublxCIzMxvojSSjV6RHi44AO3g5SxyEyW2F+jlDIZUjOLsK17CKp45CJqFUBFB4ejnnz5qG4uNhwrKioCG+++SbCw8PrLBxRY6XTC3x/c9f3x7ux94dIStZqJdp4lm97cDThhsRpyFTU6i6wjz/+GPfffz+8vb0REhICmUyGuLg4aDQa7Ny5s64zEjU6v5+5jms5xXC0UnHtEaJGIMzfESeTc3D0ShYeCvGSOg6ZgFoVQO3atcOFCxfw3Xff4ezZsxBCYOTIkRgzZgwsLS3rOiNRo/O/m70/w+/xgUalkDgNEYX5OWHVn1dw5Ap7gKh6ar0OkKWlJSZOnFiXWYhMwuX0fOy/kAGZDBjblcNfRI1BmL8jAOBcai5yi8tgp1Hd4RVk7mo1BwgAzp07hxdffBH33Xcf+vbtixdffBFnz56ty2xEjdJ3h8q3vYhs5QYfJyuJ0xARALjbaeDrZAW9AI4lZksdh0xArQqgn376Ce3atUNMTAxCQkLQoUMHxMbGon379vjxxx/rOiNRo1FYqsWPMUkAeOs7UWMT5lfeCxRzJUviJGQKajUE9uqrr2LWrFmYP3++0fF58+bhtddew7Bhw+okHFFjszXuGvKKtfB1skLvFtz6hagxCfN3wsZjyZwHRNVSqx6g1NRUPP7445WOjx07FqmpqXcdiqgxEkLg25srP4/t5gs59/0ialQq5gHFJWWjTKeXOA01drUqgPr06YP9+/dXOn7gwAFERETcdSiixig2Mduw79ewUO77RdTYNHe1gb2lCkVlOvx9LVfqONTI1WoI7KGHHsJrr72GmJgYdOvWDQBw6NAh/Pjjj3jzzTexdetWo7ZETcH/oq8AAB4M8YKjNff9Imps5HIZwvwc8fvZNBy5koUQHwepI1EjJhNCiJq+SC6vXseRTCaDTqercSip5ebmwt7eHjk5ObCzs5M6DjUCGfkl6L7oD5Tq9Nj6Yg9ufUHUSC3bcxHv7TiHge08sHxsqNRxqIHV5PO7Vj1Aej3HVsm8cN8vItNwj78TAODIlRsQQkAm41w9qlqt1wH6r+zs7Lo6FVGjotMLrPmrfO2fcdz3i6hRa9/MHhYKOTLyS5CYxc256dZqVQC9++67WL9+veHrYcOGwcnJCc2aNcPx48frLBxRY/DH2TQkZxfBwUqFB7jvF1GjplEp0N7bHgB4OzzdVq0KoM8//xw+PuV3wURFReG3337Djh07MHDgQLzyyit1GpBIat/enPw8Ioz7fhGZgooFEY9yQUS6jVrNAUpJSTEUQL/88guGDx+O/v37w9/fH127dq3TgERSis8o+GffLw5/EZmEMH8nfL7vMo4msAeIbq1WPUCOjo5ISirfDmDHjh3o27cvgPKF4kzxri+iW/nu5q7v3PeLyHSE3uwBupiWjxsFpRKnocaqVgXQo48+itGjR6Nfv37IzMzEwIEDAQBxcXFo3rx5nQYkkkpRqQ4/Hr257xd7f4hMhpO1BYJcrQEAMewFoluoVQH00Ucf4cUXX0SbNm0QFRUFGxsbAOVDY88//3ydBiSSys/HryG3Yt+vltz3i8iUGG6HT+A8IKpareYAqVQqzJgxo9LxqVOn3m0eokZjzeHyW99HdeG+X0SmJtTPEeuOJOEo7wSjW6j1OkD/+9//0LNnT3h5eSEhoXyexJIlS7Bly5Y6C0cklbOpuYhLyoZSLsNjod5SxyGiGqroATp5NQfFZZybSpXVqgBavnw5pk+fjoEDByI7O9sw8dnBwQFLliypy3xEklh3uHzuT7827nC1VUuchohqys/ZCi42FijV6XEyOUfqONQI1aoA+uSTT/Dll19izpw5UCj+WRclLCwMJ0+erLNwRFIoLtNhY+xVAMDILr4SpyGi2pDJZAjzK+8F4jAYVaVWBVB8fDw6depU6bharUZBQcFdhyKS0raTKcgt1qKZgyUimrtIHYeIainMnwsi0q3VqgAKCAhAXFxcpePbt29HcHDw3WYiklTF8NfIe3w4+ZnIhIXdnAd0NOEG9HohcRpqbGp1F9grr7yCF154AcXFxRBC4PDhw1i7di3efvttrFy5sq4zEjWYi2l5OHwlC3IZMCzMR+o4RHQX2nrZQaOSI6eoDJfS89HC3VbqSNSI1KoAeuqpp6DVavHqq6+isLAQo0ePRrNmzfDJJ58gIiKirjMSNZiK3p97W7vDw14jcRoiuhsqhRwdfRxw6HIWjly5wQKIjNT6NviJEyciISEBaWlpSE1NxeHDh3Hs2DGuBE0mq0Srw4abk59HdWHvD1FTcI9hGIzzgMhYjQqg7OxsjBkzBq6urvDy8sLSpUvh5OSEzz77DM2bN8ehQ4fw9ddf11dWonq16/R13Cgsg4edhis/EzURoYad4XknGBmr0RDY7NmzsW/fPjzxxBPYsWMHpk2bhh07dqC4uBjbtm1D79696ysnUb1be3Pl5+Fh3lAqat05SkSNSGc/R8hkQGJWIdJyi+Fmx6FtKlej3/K//vorVq1ahcWLF2Pr1q0QQqBly5b4448/WPyQSUvILMDBS5mQyYDh93D4i6ipsNOo0NrDDkD53WBEFWpUAF27dg1t2rQBAAQGBkKj0WDChAn1EoyoIa07Uj75uVcLV3g7WkmchojqUtjNYbAjXA+I/qVGBZBer4dKpTJ8rVAoYG1tXeehiBpSmU6PH49y8jNRU1WxIGIMe4DoX2o0B0gIgSeffBJqdfneSMXFxZg0aVKlImjjxo11l5Conv1+5joy8kvgYqPGfcHuUschojpWsSDi6Wu5KCjRwlpdqxVgqImp0U/BE088YfT12LFj6zQMkRTW3lz7Z1iYN1Sc/EzU5DRzsISXvQbXcopxPCkb3bnFDaGGBdCqVavqKweRJK7eKMS+C+kAyre+IKKmKdTfCdeOX8ORKzdYABGAu1gIkagp+OFIEoQAugc5w8+Z89mImqp7KjZG5YKIdBMLIDJbWp0ePxgmP/tKnIaI6lOYX/k8oNiEG9Dq9BKnocaABRCZrb3n05GaWwxHKxX6t+XkZ6KmrJWHLWzVShSU6nA2NU/qONQIsAAis1Wx8vPQzt5QKxUSpyGi+qSQy9DJj7fD0z9YAJFZSs0pxh9n0wAAIzn8RWQWuCAi/ZvkBdCyZcsQEBAAjUaD0NBQ7N+//7bt9+7di9DQUGg0GgQGBmLFihW3bLtu3TrIZDI8/PDDdZyaTN2PR5OgF0AXfyc0d7OROg4RNYCKAuhYYra0QahRkLQAWr9+PaZOnYo5c+bg2LFjiIiIwMCBA5GYmFhl+/j4eAwaNAgRERE4duwYZs+ejcmTJ2PDhg2V2iYkJGDGjBmIiIio77dBJkavF4atL0Zy5Wcis9HBxwEyGZCcXYTrucVSxyGJSVoAffjhhxg/fjwmTJiA4OBgLFmyBD4+Pli+fHmV7VesWAFfX18sWbIEwcHBmDBhAp5++mksXrzYqJ1Op8OYMWPw5ptvIjAwsCHeCpmQAxczkJxdBDuNEoPae0odh4gaiI1aiVbutgDYC0QSFkClpaWIiYlB//79jY73798fBw8erPI10dHRldoPGDAAR48eRVlZmeHY/Pnz4erqivHjx1crS0lJCXJzc40e1HStP1re+/Nwp2bQqDj5mcicdPJ1AADEJWVLmoOkJ1kBlJGRAZ1OB3d349uP3d3dkZqaWuVrUlNTq2yv1WqRkZEBAPjzzz+xcuVKfPnll9XOsmjRItjb2xsePj4cFmmqbhSUIur0dQDA8DD+fyYyN518KuYB8U4wcyf5JGiZTGb0tRCi0rE7ta84npeXh7Fjx+LLL7+Ei0v1lzqfNWsWcnJyDI+kpKQavAMyJZvjklGq06Otlx3aNbOXOg4RNbCKHqATV3O4IKKZk2xLXBcXFygUikq9PWlpaZV6eSp4eHhU2V6pVMLZ2RmnT5/GlStX8OCDDxqe1+vLf8CVSiXOnTuHoKCgSudVq9WGHe6p6RJCYP3Nyc8juO8XkVkKcrWBrVqJvBItzl3PQ1sv/kPIXEnWA2RhYYHQ0FBERUUZHY+KikL37t2rfE14eHil9rt27UJYWBhUKhVat26NkydPIi4uzvB46KGHEBkZibi4OA5tmbmTyTk4m5oHC6UcQ0KaSR2HiCQgl8sQ4uMAgBOhzZ1kPUAAMH36dIwbNw5hYWEIDw/HF198gcTEREyaNAlA+dBUcnIyvv32WwDApEmT8Omnn2L69OmYOHEioqOjsXLlSqxduxYAoNFo0K5dO6Pv4eDgAACVjpP5qej9ub+tB+ytVBKnISKpdPJ1wIGLGTiWmI2x3fykjkMSkbQAGjFiBDIzMzF//nykpKSgXbt22LZtG/z8yn8gU1JSjNYECggIwLZt2zBt2jR89tln8PLywtKlSzF06FCp3gKZiKJSHbbGXQPA4S8ic1cxD+hYEidCmzOZqJhFTAa5ubmwt7dHTk4O7OzspI5DdWBj7FVM/+E4fJwssXdGJOTyW0+0J6KmLaugFJ0XlE+nOP5//dkj3ITU5PNb8rvAiBrCDzfX/hkW6sPih8jMOVlbwN/ZCgAQdzVb2jAkGRZA1OQlZBbg0OUsyGTAY6HeUschokagky/XAzJ3LICoyavo/Ylo4QovB0uJ0xBRY2CYB8Q7wcwWCyBq0rQ6PX6KuQoAGMGVn4nopooVoeOSsqHXcyqsOWIBRE3avgvpuJ5bAkcrFfq2cZM6DhE1Eq09baFWypFTVIb4zAKp45AEWABRk1ax9s8jnbyhVnLjUyIqp1LI0f7mdjgcBjNPLICoycrIL8HvZ9IAcO0fIqrsn3lAnAhtjlgAUZO1KTYZWr1AiI8DWnnYSh2HiBqZijvB4pKypQ1CkmABRE2SEALrb979NTyMt74TUWUVPUBnU/NQWKqVNgw1OBZA1CTFJmbjYlo+NCo5HgzxkjoOETVCnvaW8LDTQKcXOHk1R+o41MBYAFGT9MPNyc+D2nvCTsNl7omoav/sC5YtaQ5qeCyAqMkpKNHilxM3Nz7l2j9EdBucCG2+WABRk/PryRQUlOoQ4GKNLgFOUschokasYiJ0bGI2uDe4eWEBRE1OxfDXsDBvyGTc+JSIbq2dlz0UchnS80pwLadY6jjUgFgAUZNyMS0fRxNuQC4Dhnbm3V9EdHuWFgoEe5Yvk8FhMPPCAoialB9v3voe2coN7nYaidMQkSkw7AvGFaHNCgsgajLKdHpsiE0GAAznys9EVE28E8w8sQCiJmPPuXRk5JfAxcYC97bmxqdEVD0VE6FPJuegVKuXOA01FBZA1GT8cLRi49NmUCn4o01E1ePvbAUHKxVKtXqcScmVOg41EH5KUJOQnleC3WfLNz4dxrV/iKgGZDIZOvk4AOBEaHPCAoiahM3Hyjc+7ejjgJbu3PiUiGqmYhiM84DMBwsgMnlCCMPw1zBufEpEtdDR0AOULWkOajgsgMjkHb+agwtp+VArufEpEdVOyM0CKDGrEJn5JdKGoQbBAohMXkXvDzc+JaLasrdUobmbDQAgjsNgZoEFEJm0olIdfo4r3/h0WCiHv4io9jpxGMyssAAik7bzdCrySrTwdrREt0BnqeMQkQn7ZyI07wQzByyAyKT9GFM+/PVYqDfkcm58SkS1V7Ei9PGkHOj03Bm+qWMBRCYrKasQf17MhExWXgAREd2Nlu62sLJQIL9Ei4tp+VLHoXrGAohM1k8xVwEAPYJc4O1oJXEaIjJ1CrkMId4OALggojlgAUQmSa8XhgKIa/8QUV0xbIzKidBNHgsgMknRlzORnF0EW40SA9p6SB2HiJqIigUReSt808cCiEzSjzfX/hnS0QsalULiNETUVHS82QN0Pi0PecVl0oahesUCiExOTlEZtp9KBQAMC+XGp0RUd9xsNfB2tIQQwImrOVLHoXrEAohMzs/Hr6FEq0crd1t08LaXOg4RNTEV6wHFJnAidFPGAohMzo//mvwsk3HtHyKqW2F+5QXQX/FZEieh+sQCiEzKudQ8HE/KhlIuw8Odmkkdh4iaoO5B5avKH7mShRKtTuI0VF9YAJFJqZj8fF+wG1xs1BKnIaKmqLmbDVxs1CjR6nk7fBPGAohMRplOj03HkgFw8jMR1R+ZTGboBTp4KVPiNFRfWACRyfjjbBoyC0rhaqtGn1auUschoiasogCKvpQhcRKqLyyAyGT8eLR88vOjnZtBqeCPLhHVn+5BLgDKF0QsLNVKnIbqAz9FyCSk5RVj97k0ABz+IqL65+NkiWYOlijTCRy9wtvhmyIWQGQSNsUmQ6cX6OzrgOZuNlLHIaImjvOAmj4WQNToCSHww827v4aFsfeHiBpGOOcBNWksgKjROxyfhUvpBbCyUODBEC+p4xCRmagogE4m5yCniPuCNTUsgKjRW3M4EUD5xqc2aqXEaYjIXHjaWyLQxRp6Uf4PMWpaWABRo3ajoBTbT5ZvfDq6i5/EaYjI3IQb5gFxGKypYQFEjdqG2Kso1enRrpkd2nPjUyJqYBW3w0dzInSTwwKIGi0hhGH4i70/RCSFboFOAICzqXnIyC+ROA3VJRZA1Gj9FZ+Fy+kFsLZQ4KGOnPxMRA3P2UaN1h62AIBDl9kL1JSwAKJGa81f5b0/D3VsxsnPRCSZimEwrgfUtLAAokYpq6AUO06VT34e09VX4jREZM4qFkQ8xAKoSZG8AFq2bBkCAgKg0WgQGhqK/fv337b93r17ERoaCo1Gg8DAQKxYscLo+S+//BIRERFwdHSEo6Mj+vbti8OHD9fnW6B6sCGmfPJz+2b2aNeMk5+JSDpdAp0glwGXMwqQklMkdRyqI5IWQOvXr8fUqVMxZ84cHDt2DBERERg4cCASExOrbB8fH49BgwYhIiICx44dw+zZszF58mRs2LDB0GbPnj0YNWoUdu/ejejoaPj6+qJ///5ITk5uqLdFd0kIgbUVk5/Z+0NEErPTqND+5j/EeDdY0yETQgipvnnXrl3RuXNnLF++3HAsODgYDz/8MBYtWlSp/WuvvYatW7fizJkzhmOTJk3C8ePHER0dXeX30Ol0cHR0xKefforHH3+8Wrlyc3Nhb2+PnJwc2NnZ1fBd0d2KvpSJUV8egrWFAn/N6cv5P0QkuXe2n8WKvZfwWKg3Fg8LkToO3UJNPr8l6wEqLS1FTEwM+vfvb3S8f//+OHjwYJWviY6OrtR+wIABOHr0KMrKql6mvLCwEGVlZXBycrpllpKSEuTm5ho9SDqGlZ87cfIzETUO3Q37gmVCwn4DqkOSFUAZGRnQ6XRwd3c3Ou7u7o7U1NQqX5Oamlple61Wi4yMqlfpnDlzJpo1a4a+ffveMsuiRYtgb29vePj4cMNNqWTml2DnqYqVnzn8RUSNQ5i/I1QKGZKzi5CYVSh1HKoDkk+ClslkRl8LISodu1P7qo4DwHvvvYe1a9di48aN0Gg0tzznrFmzkJOTY3gkJSXV5C1QHapY+bmDNyc/E1HjYWWhRCcfRwC8Hb6pkKwAcnFxgUKhqNTbk5aWVqmXp4KHh0eV7ZVKJZydnY2OL168GG+//TZ27dqFDh063DaLWq2GnZ2d0YMaXvnk5/Lik70/RNTY/LMvGAugpkCyAsjCwgKhoaGIiooyOh4VFYXu3btX+Zrw8PBK7Xft2oWwsDCoVCrDsffffx8LFizAjh07EBYWVvfhqV5EX85EfEYBbNRKPBjClZ+JqHHhPKCmRdIhsOnTp+Orr77C119/jTNnzmDatGlITEzEpEmTAJQPTf37zq1JkyYhISEB06dPx5kzZ/D1119j5cqVmDFjhqHNe++9h7lz5+Lrr7+Gv78/UlNTkZqaivz8/AZ/f1QzFSs/D+noBWtOfiaiRqajrwM0Kjky8ktwMY2fKaZO0gJoxIgRWLJkCebPn4+OHTti37592LZtG/z8yje+TElJMVoTKCAgANu2bcOePXvQsWNHLFiwAEuXLsXQoUMNbZYtW4bS0lI89thj8PT0NDwWL17c4O+Pqi8zvwQ7T5cPb47i8BcRNUJqpQJhfuV3FHMYzPRJug5QY8V1gBre53svYdH2swjxtseWF3tKHYeIqEqf7b6I93eew4C27vh8HKdYNDYmsQ4QUQWu/ExEpsKwL9jlLOj07D8wZSyASHLRlzJxJbMQNmolHujAyc9E1Hi1b2YPG7USOUVlOJPCRXNNGQsgklzFys8Pd+LkZyJq3JQKOboGVMwDqnoBXjINLIBIUhmc/ExEJobrATUNLIBIUj/FXEWZTiDExwFtvbjyMxE1ft2DXAAAh+OzUKbTS5yGaosFEEmmRKvDqj/jAQBjOfmZiExEaw9bOFqpUFiqw4mr2VLHoVpiAUSS2XLsGq7nlsDdTo2HOnLyMxGZBrlc9s8w2EUOg5kqFkAkCb1eYMW+SwCA8T0DoFYqJE5ERFR94YE3t8W4zALIVLEAIklEnbmOy+kFsNUoOfmZiExO+M15QEcTbqC4TCdxGqoNFkDU4IQQWLG3vPdnXDc/2GpUd3gFEVHjEuRqDTdbNUq1ehy9ckPqOFQLLICowR2Oz8KxxGxYKOV4qkeA1HGIiGpMJpPhvmA3AMD3fyVInIZqgwUQNbiK3p9hod5wtVVLnIaIqHYq/gG343QqEjILJE5DNcUCiBrUmZRc7D6XDrkMeKZXoNRxiIhqraW7Lfq0coUQwMoD8VLHoRpiAUQN6vObvT8D23vCz9la4jRERHfnmYjyf8j9cDQJNwpKJU5DNcECiBpMUlYhfj6RAgB4rneQxGmIiO5eeJAz2njaobhMz7lAJoYFEDWYlQfiodML9GzugnbNuO0FEZk+mUxmGM5ffTCBt8SbEBZA1CCyCkqx7kj5ru+T2PtDRE3I4A6e8LTXICO/BFvikqWOQ9XEAogaxDcHr6C4TI92zezQo7mz1HGIiOqMSiHH0zfvCPtyfzz0eiFxIqoOFkBU7wpLtfgm+goA4LnezSGTyaQNRERUx0Z28YGtWomLafnYcz5N6jhUDSyAqN6tO5yE7MIy+Dtb4f52HlLHISKqc7YaFUZ28QEAfLmPt8SbAhZAVK/KdHrD+hgTewVCIWfvDxE1TU/1CIBSLkP05UycvJojdRy6AxZAVK9+Pn4NydlFcLFRY2hnb6njEBHVGy8HSzzQwRMA8OX+yxKnoTthAUT1RgiBz/eW/xJ4qoc/NCqFxImIiOrXhJsLI/56MgXJ2UUSp6HbYQFE9Wb3uTScu54HG7USY7v5SR2HiKjetWtmjx7NnaHTC6zi9hiNGgsgqjcr9pT3/ozu6gt7S5XEaYiIGkZFL9Daw4nIKSqTOA3dCgsgqhcHL2Xg8JUsqBQyw/oYRETmoE9LV7Rws0FBqQ7rDidKHYdugQUQ1bmM/BJMX38cADAszAce9hqJExERNRyZTIaJN7fHWPXnFZRq9RInoqqwAKI6pdMLTFl3DKm5xQhytcbsQcFSRyIianBDOnrB1VaN1Nxi/HrymtRxqAosgKhOfRR1Hn9ezISVhQIrxobCRq2UOhIRUYNTKxV4srs/AOCLffEQgttjNDYsgKjO/H7mOj7dfREAsOjR9mjhbitxIiIi6Yzp6gtLlQJnUnLx58VMqePQf7AAojqRmFmIaevjAABPdvfHkI7NpA1ERCQxBysLjLinfHuML7gwYqPDAojuWnGZDs99H4PcYi06+Tpw3g8R0U1P9wiAXAbsO5+OH44mSR2H/oUFEN21N7aexulruXCytsBnozvDQskfKyIiAPB1tjLcETZzwwlsPc4J0Y0FP6norvxwNAnrjiRBJgM+HtkRXg6WUkciImpUZt7fGqO6+EIvgGnr47DrdKrUkQgsgOgunL6Wg9c3nwIATO/bEhEtXCVORETU+MhkMix8uB0e7dQMOr3Ai2uOYe/5dKljmT0WQFQrOUVleO67WJRo9Yhs5YoXIptLHYmIqNGSy2V477EOGNTeA6U6PZ759iiiL/HOMCmxAKIa0+sFXv7hOBKzCtHMwRIfjegIuVwmdSwiokZNqZBjyYhOuLe1G0q0eoz/5ghiE29IHctssQCiGvt832X8duY6LBRyrBgbCgcrC6kjERGZBAulHMvGdEbP5i4oLNXhia8P41RyjtSxzBILIKo2IQS+PhCP93eeBQC88VBbtPe2lzgVEZFp0agU+OLxUNzj74i8Yi3GrfwL56/nSR3L7LAAomop0erw2oYTmP/L39ALYFw3P4zq4iN1LCIik2RlocTXT96DEG973Cgsw5iv/kJ8RoHUscwKCyC6o/S8Eoz+8i/8cPQq5DJg7uBgzB/SFjIZ5/0QEdWWrUaFb57ugtYetkjPK8GYLw8hKatQ6lhmgwUQ3dap5Bw89OkBxCTcgK1GiVVPdcGEiEAWP0REdcDBygLfTeiKIFdrXMspxtDlB7HjVIrUscwCCyC6pV9PpOCxFQeRklOMQBdrbHmhB3q35Fo/RER1ycVGje8ndEOQqzXS8kow6btYTPz2KFJyiqSO1qSxAKJK9HqBD3edwwtrYlFcpkevlq7Y9EIPBLraSB2NiKhJ8rDX4NfJEXgxsjmUchmi/r6Ovh/sxeo/46HTC6njNUkyIQSv7H/k5ubC3t4eOTk5sLOzkzpOgyoo0ZYv1f73dQDAxIgAzBwYDAXX+SEiahDnUvMwa+MJxCZmAwBCfBzwzqPtEexpXp9HtVGTz28WQFUw1wIoKasQE789irOpebBQyPH2o+3xWKi31LGIiMyOXi/w/eFEvLf9LPJKtFDKZZjYKxBT7msBjUohdbxGiwXQXTKnAkgIgWNJ2fguOgG/nExBqVYPFxs1Ph8XilA/R6njERGZteu5xXhj62lsP1W+gaqfsxUWPtwePVu4SJyscWIBdJfMoQAqKNFiS9w1fHcoAX+n5BqOh/k54pPRneBpz13diYgai12nU/F/W04jNbcYANCrpSuGh3mjXxt3qJXsEarAAuguNeUC6Pz1PHx3KAGbYpORV6IFAKiVcjzQwQtju/mio48Db3EnImqE8ku0WLzzHL6JvoKKT24HKxWGhHhhWJgP2nrZmf3vbxZAd6mpFUDFZTrs+vs6vjuUgMPxWYbjAS7WGNPVF4+FenM/LyIiE3ElowA/xiRhQ0yyoUcIAFp72GJYmA8e7ugFZxu1hAmlwwLoLpl6AVSi1eF4Ug6iL2Xi0OVMxCTeQKlWDwBQyGXoG+yGsd380CPIhbu4ExGZKJ1e4MDFDPx4NAm7/r5u+D2vUshwb2s3PBbqgw7e9nC1UZvN73qTKoCWLVuG999/HykpKWjbti2WLFmCiIiIW7bfu3cvpk+fjtOnT8PLywuvvvoqJk2aZNRmw4YNeP3113Hp0iUEBQVh4cKFeOSRR6qdydQKoFKtHieTsxF9KRPRlzMRk3ADxWV6ozae9hoMD/PBqC6+8LDXSJSUiIjqQ05hGbYeT8aPMVdx4qrx7vIWCjmaOVrC29ES3o5W8HEq/6+3oyV8HK3gYmPRZIbOavL5rWygTFVav349pk6dimXLlqFHjx74/PPPMXDgQPz999/w9fWt1D4+Ph6DBg3CxIkT8d133+HPP//E888/D1dXVwwdOhQAEB0djREjRmDBggV45JFHsGnTJgwfPhwHDhxA165dG/ot1qm84jIkZRXh6o1CJN0o/+/FtHwcvXIDRWU6o7YuNhboGuiM8EBnhAc5I9DFusn8gBMRkTF7KxXGhftjXLg/zqbm4sejVxH193UkZxehVKdHfEbBLTdbtVDIYWepgp2lEnYaVfmfNUrYWapgb6m6eeyf5+z/9bydRgULpWmuqSxpD1DXrl3RuXNnLF++3HAsODgYDz/8MBYtWlSp/WuvvYatW7fizJkzhmOTJk3C8ePHER0dDQAYMWIEcnNzsX37dkOb+++/H46Ojli7dm21ctVXD1CpVo+M/BKUaPUo0epQUqav+s9aPQpLdUjJLsLVG0VIulGIqzeKkFNUdstzO1lboFugE7rdLHqau9mw4CEiMnNanR4pOcVGnyVXbxTi6s1/TKfkFuNuqwCNSg47zc3C6D/FkX2lwuqfYw6WFrC3UtXNG73JJHqASktLERMTg5kzZxod79+/Pw4ePFjla6Kjo9G/f3+jYwMGDMDKlStRVlYGlUqF6OhoTJs2rVKbJUuW3DJLSUkJSkpKDF/n5ubesu3dOH41G8NWRN/VOZysLQzdlt6OlvBxskKYvyNautmazRgvERFVj1Ihh4+TFXycrBAO50rPl2r1SM8vQW5RWfmjWIscw5/LkFukvfnfsvLjxVrDc3nF5XcSF5fpUVxWgrS8kkrnv512zezwy0u3nvJS3yQrgDIyMqDT6eDu7m503N3dHampqVW+JjU1tcr2Wq0WGRkZ8PT0vGWbW50TABYtWoQ333yzlu+k+tRKOSwUcqiVcqhVcqiVipt/vvnf//zZw04DHycrw7itt6MlrNWSjloSEVETYqGUo5mDJZo51HztN51eIL+4vEDK+XfBZPjzPwVTThUFlZ2mbnt/akryT9P/DtMIIW47dFNV+/8er+k5Z82ahenTpxu+zs3NhY+Pz53D11AHbwecXziwzs9LRETU0BRyGeytVLC3UqE2n5h6vajyM7yqY/VBsgLIxcUFCoWiUs9MWlpapR6cCh4eHlW2VyqVcHZ2vm2bW50TANRqNdRq81wzgYiISAq3mrbRUPNXJZu6bWFhgdDQUERFRRkdj4qKQvfu3at8TXh4eKX2u3btQlhYGFQq1W3b3OqcREREZH4kHQKbPn06xo0bh7CwMISHh+OLL75AYmKiYV2fWbNmITk5Gd9++y2A8ju+Pv30U0yfPh0TJ05EdHQ0Vq5caXR315QpU9CrVy+8++67GDJkCLZs2YLffvsNBw4ckOQ9EhERUeMjaQE0YsQIZGZmYv78+UhJSUG7du2wbds2+Pn5AQBSUlKQmJhoaB8QEIBt27Zh2rRp+Oyzz+Dl5YWlS5ca1gACgO7du2PdunWYO3cuXn/9dQQFBWH9+vUmvwYQERER1R3JV4JujExtJWgiIiKq2ee3aS7fSERERHQXWAARERGR2WEBRERERGaHBRARERGZHRZAREREZHZYABEREZHZYQFEREREZocFEBEREZkdFkBERERkdiTdCqOxqlgcOzc3V+IkREREVF0Vn9vV2eSCBVAV8vLyAAA+Pj4SJyEiIqKaysvLg729/W3bcC+wKuj1ely7dg22traQyWQN+r1zc3Ph4+ODpKQk7kNWA7xutcdrVzu8brXD61Z7vHZ3JoRAXl4evLy8IJfffpYPe4CqIJfL4e3tLWkGOzs7/oDXAq9b7fHa1Q6vW+3wutUer93t3annpwInQRMREZHZYQFEREREZocFUCOjVqsxb948qNVqqaOYFF632uO1qx1et9rhdas9Xru6xUnQREREZHbYA0RERERmhwUQERERmR0WQERERGR2WAARERGR2WEBVMeWL1+ODh06GBaqCg8Px/bt2w3PCyHwxhtvwMvLC5aWlujTpw9Onz5tdI6SkhK89NJLcHFxgbW1NR566CFcvXrVqM2NGzcwbtw42Nvbw97eHuPGjUN2dnZDvMV6cbvrVlZWhtdeew3t27eHtbU1vLy88Pjjj+PatWtG5zDH6wbc+Wfu35599lnIZDIsWbLE6Lg5XrvqXLczZ87goYcegr29PWxtbdGtWzckJiYanjfH6wbc+drl5+fjxRdfhLe3NywtLREcHIzly5cbncNcr92/LVq0CDKZDFOnTjUc42dEAxJUp7Zu3Sp+/fVXce7cOXHu3Dkxe/ZsoVKpxKlTp4QQQrzzzjvC1tZWbNiwQZw8eVKMGDFCeHp6itzcXMM5Jk2aJJo1ayaioqJEbGysiIyMFCEhIUKr1Rra3H///aJdu3bi4MGD4uDBg6Jdu3bigQceaPD3W1dud92ys7NF3759xfr168XZs2dFdHS06Nq1qwgNDTU6hzleNyHu/DNXYdOmTSIkJER4eXmJjz76yOg5c7x2d7puFy9eFE5OTuKVV14RsbGx4tKlS+KXX34R169fN5zDHK+bEHe+dhMmTBBBQUFi9+7dIj4+Xnz++edCoVCIzZs3G85hrteuwuHDh4W/v7/o0KGDmDJliuE4PyMaDgugBuDo6Ci++uorodfrhYeHh3jnnXcMzxUXFwt7e3uxYsUKIYQQ2dnZQqVSiXXr1hnaJCcnC7lcLnbs2CGEEOLvv/8WAMShQ4cMbaKjowUAcfbs2QZ6V/Wv4rpV5fDhwwKASEhIEELwuv3Xf6/d1atXRbNmzcSpU6eEn5+fUQHEa/ePf1+3ESNGiLFjx96yLa+bsX9fu7Zt24r58+cbPd+5c2cxd+5cIQSvXV5enmjRooWIiooSvXv3NhRA/IxoWBwCq0c6nQ7r1q1DQUEBwsPDER8fj9TUVPTv39/QRq1Wo3fv3jh48CAAICYmBmVlZUZtvLy80K5dO0Ob6Oho2Nvbo2vXroY23bp1g729vaGNKfvvdatKTk4OZDIZHBwcAPC6Vajq2un1eowbNw6vvPIK2rZtW+k1vHaVr5ter8evv/6Kli1bYsCAAXBzc0PXrl2xefNmw2t43cpV9TPXs2dPbN26FcnJyRBCYPfu3Th//jwGDBgAgNfuhRdewODBg9G3b1+j4/yMaFjcDLUenDx5EuHh4SguLoaNjQ02bdqENm3aGH7w3N3djdq7u7sjISEBAJCamgoLCws4OjpWapOammpo4+bmVun7urm5GdqYoltdt/8qLi7GzJkzMXr0aMOGgOZ83YDbX7t3330XSqUSkydPrvK15nztbnXdUlNTkZ+fj3feeQdvvfUW3n33XezYsQOPPvoodu/ejd69e5v1dQNu/zO3dOlSTJw4Ed7e3lAqlZDL5fjqq6/Qs2dPAOb9M7du3TrExsbiyJEjlZ6reF/8jGgYLIDqQatWrRAXF4fs7Gxs2LABTzzxBPbu3Wt4XiaTGbUXQlQ69l//bVNV++qcpzG71XX7dxFUVlaGkSNHQq/XY9myZXc8pzlcN+DW166oqAgff/wxYmNja/wezeHa3eq6VfQsDhkyBNOmTQMAdOzYEQcPHsSKFSvQu3fvW57THK4bcPu/r0uXLsWhQ4ewdetW+Pn5Yd++fXj++efh6elZqdfj35r6tUtKSsKUKVOwa9cuaDSaW7bjZ0TD4BBYPbCwsEDz5s0RFhaGRYsWISQkBB9//DE8PDwAoFIFnpaWZqj4PTw8UFpaihs3bty2zfXr1yt93/T09Er/cjAlt7puFcrKyjB8+HDEx8cjKirK0PsDmPd1A2597fbv34+0tDT4+vpCqVRCqVQiISEBL7/8Mvz9/QGY97W71XVzcXGBUqms1AMZHBxsuAvMnK8bcOtrV1RUhNmzZ+PDDz/Egw8+iA4dOuDFF1/EiBEjsHjxYgDme+1iYmKQlpaG0NBQw9/HvXv3YunSpVAqlYb3xc+IhsECqAEIIVBSUoKAgAB4eHggKirK8FxpaSn27t2L7t27AwBCQ0OhUqmM2qSkpODUqVOGNuHh4cjJycHhw4cNbf766y/k5OQY2jQFFdcN+Kf4uXDhAn777Tc4OzsbteV1M1Zx7caNG4cTJ04gLi7O8PDy8sIrr7yCnTt3AuC1+7eK62ZhYYF77rkH586dM3r+/Pnz8PPzA8Dr9l8V166srAxlZWWQy40/XhQKBfR6PQDzvXb33XcfTp48afT3MSwsDGPGjEFcXBwCAwP5GdGQGnbOddM3a9YssW/fPhEfHy9OnDghZs+eLeRyudi1a5cQovwWR3t7e7Fx40Zx8uRJMWrUqCpvcfT29ha//fabiI2NFffee2+Vtzh26NBBREdHi+joaNG+fXuTvsXxdtetrKxMPPTQQ8Lb21vExcWJlJQUw6OkpMRwDnO8bkLc+Wfuv/57F5gQ5nnt7nTdNm7cKFQqlfjiiy/EhQsXxCeffCIUCoXYv3+/4RzmeN2EuPO16927t2jbtq3YvXu3uHz5sli1apXQaDRi2bJlhnOY67X7r3/fBSYEPyMaEgugOvb0008LPz8/YWFhIVxdXcV9991n9EGk1+vFvHnzhIeHh1Cr1aJXr17i5MmTRucoKioSL774onBychKWlpbigQceEImJiUZtMjMzxZgxY4Stra2wtbUVY8aMETdu3GiIt1gvbnfd4uPjBYAqH7t37zacwxyvmxB3/pn7r6oKIHO8dtW5bitXrhTNmzcXGo1GhISEGK1jI4R5Xjch7nztUlJSxJNPPim8vLyERqMRrVq1Eh988IHQ6/WGNuZ67f7rvwUQPyMajkwIIaTsgSIiIiJqaJwDRERERGaHBRARERGZHRZAREREZHZYABEREZHZYQFEREREZocFEBEREZkdFkBERERkdlgAEZFZeOONN9CxY0epYxjIZDJs3rxZ6hhEZosFEBHVqRUrVsDW1hZardZwLD8/HyqVChEREUZt9+/fD5lMhvPnzzd0zAbT2AovIirHAoiI6lRkZCTy8/Nx9OhRw7H9+/fDw8MDR44cQWFhoeH4nj174OXlhZYtW0oRlYjMGAsgIqpTrVq1gpeXF/bs2WM4tmfPHgwZMgRBQUE4ePCg0fHIyEh89913CAsLg62tLTw8PDB69GikpaUBAPR6Pby9vbFixQqj7xMbGwuZTIbLly8DAHJycvDMM8/Azc0NdnZ2uPfee3H8+PHbZl21ahWCg4Oh0WjQunVrLFu2zPDclStXIJPJsHHjRkRGRsLKygohISGIjo42OseXX34JHx8fWFlZ4ZFHHsGHH34IBwcHAMDq1avx5ptv4vjx45DJZJDJZFi9erXhtRkZGXjkkUdgZWWFFi1aYOvWrdW+zkR0d1gAEVGd69OnD3bv3m34evfu3ejTpw969+5tOF5aWoro6GhERkaitLQUCxYswPHjx7F582bEx8fjySefBADI5XKMHDkS33//vdH3WLNmDcLDwxEYGAghBAYPHozU1FRs27YNMTEx6Ny5M+677z5kZWVVmfHLL7/EnDlzsHDhQpw5cwZvv/02Xn/9dXzzzTdG7ebMmYMZM2YgLi4OLVu2xKhRowzDe3/++ScmTZqEKVOmIC4uDv369cPChQsNrx0xYgRefvlltG3bFikpKUhJScGIESMMz7/55psYPnw4Tpw4gUGDBmHMmDG3zEtEdUzizViJqAn64osvhLW1tSgrKxO5ublCqVSK69evi3Xr1onu3bsLIYTYu3evACAuXbpU6fWHDx8WAEReXp4QQojY2Fghk8nElStXhBBC6HQ60axZM/HZZ58JIYT4/fffhZ2dnSguLjY6T1BQkPj888+FEELMmzdPhISEGJ7z8fERa9asMWq/YMECER4eLoQQIj4+XgAQX331leH506dPCwDizJkzQgghRowYIQYPHmx0jjFjxgh7e3vD1//9vhUAiLlz5xq+zs/PFzKZTGzfvr1SWyKqe+wBIqI6FxkZiYKCAhw5cgT79+9Hy5Yt4ebmht69e+PIkSMoKCjAnj174Ovri8DAQBw7dgxDhgyBn58fbG1t0adPHwBAYmIiAKBTp05o3bo11q5dCwDYu3cv0tLSMHz4cABATEwM8vPz4ezsDBsbG8MjPj4ely5dqpQvPT0dSUlJGD9+vFH7t956q1L7Dh06GP7s6ekJAIbhuXPnzqFLly5G7f/79e38+9zW1tawtbU1nJuI6pdS6gBE1PQ0b94c3t7e2L17N27cuIHevXsDADw8PBAQEIA///wTu3fvxr333ouCggL0798f/fv3x3fffQdXV1ckJiZiwIABKC0tNZxzzJgxWLNmDWbOnIk1a9ZgwIABcHFxAVA+T8jT09No3lGFivk4/6bX6wGUD4N17drV6DmFQmH0tUqlMvxZJpMZvV4IYThWQQhRnUtU6dwV5684NxHVLxZARFQvIiMjsWfPHty4cQOvvPKK4Xjv3r2xc+dOHDp0CE899RTOnj2LjIwMvPPOO/Dx8QEAozvIKowePRpz585FTEwMfvrpJyxfvtzwXOfOnZGamgqlUgl/f/87ZnN3d0ezZs1w+fJljBkzptbvsXXr1jh8+LDRsf9mt7CwgE6nq/X3IKL6wQKIiOpFZGQkXnjhBZSVlRl6gIDyAui5555DcXExIiMjodFoYGFhgU8++QSTJk3CqVOnsGDBgkrnCwgIQPfu3TF+/HhotVoMGTLE8Fzfvn0RHh6Ohx9+GO+++y5atWqFa9euYdu2bXj44YcRFhZW6XxvvPEGJk+eDDs7OwwcOBAlJSU4evQobty4genTp1frPb700kvo1asXPvzwQzz44IP4448/sH37dqNeIX9/f8THxyMuLg7e3t6wtbWFWq2uyaUkonrAOUBEVC8iIyNRVFSE5s2bw93d3XC8d+/eyMvLQ1BQEHx8fODq6orVq1fjxx9/RJs2bfDOO+9g8eLFVZ5zzJgxOH78OB599FFYWloajstkMmzbtg29evXC008/jZYtW2LkyJG4cuWK0ff+twkTJuCrr77C6tWr0b59e/Tu3RurV69GQEBAtd9jjx49sGLFCnz44YcICQnBjh07MG3aNGg0GkOboUOH4v7770dkZCRcXV0N85iISFoyUZMBayIiuq2JEyfi7Nmz2L9/v9RRiOg2OARGRHQXFi9ejH79+sHa2hrbt2/HN998Y7SgIhE1TuwBIiK6C8OHD8eePXuQl5eHwMBAvPTSS5g0aZLUsYjoDlgAERERkdnhJGgiIiIyOyyAiIiIyOywACIiIiKzwwKIiIiIzA4LICIiIjI7LICIiIjI7LAAIiIiIrPDAoiIiIjMDgsgIiIiMjv/D159cXMi5czMAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "filter = curves[1]\n", @@ -340,20 +149,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import h5py\n", @@ -373,20 +171,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(25, 25, 3721)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "datacube.shape" @@ -401,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -412,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -422,17 +209,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(25, 25)\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "convolved = convolve_filter_with_spectra(filter, datacube, wave)\n", @@ -441,30 +220,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -481,110 +239,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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GoxG33nqr2Lp1qzxLX4jLdyn4+PiIxsbGbtt23333CS8vL3mRJ1xxN8SVSkpK5LZ03A0xb948q3Z1qK+vF7///e/FtGnThFarFV5eXsLPz09ERUWJ9evXi0uXLsmxHbPnOw4vLy8xfPhwER0dLZ588klx/Phxxa9nU1OTeOaZZ8Ttt98uRo4cKXx8fISvr6+46aabxDPPPCO+//57q8e9+nW29e8xf/58IcQPd0M899xz4umnnxbXXXed8PHxETfffLP44IMPrB6/446HM2fOdHpO3d0NMWvWrE6xXf0bdbTj+eeftzr/73//W8ydO1cEBwcLb29vodVqxe233y7fGWMvZ+6GMJlM4uGHHxYjRowQkiQJAKKurk6+/vLLL4uoqCjh6+sr1Gq1GDt2rHjwwQfFp59+2mP9RIOdJISNRf6J+llbWxuCg4Pxpz/9CYsWLerv5vSb48ePIzw8HM8//zyWLl3a380hokGGwxA0oPn4+Dh0RwMREfU+JgtE1OcuXbpk87qHh4dDS5ITkWtxGIKI+lxPtyPOnz/f7qWhicj12LNARH2uqqrK5vWrb5clooGFPQtERERkEwcJiYiIyKYBNwxhsVhw6tQp+Pn5cdlVIiI3JIRAc3MzdDpdn05cvXjxItra2px+HB8fH6sN2qizAZcsnDp1St6ciIiI3Fd9fb3N5cudcfHiRYSPHgZ9Y/d7zthLq9Wirq6OCYMNAy5Z8PPzAwD8Ar+CF7z7uTVEg4gTPXkeauUfspbWi8oKcrrVgHUJ7ajALvnzvC+0tbVB32hGXfVo+Psp770wNlsQHvkN2tramCzYMOCShY6hBy94w0tiskDkMs4kC1LnzbzsZZGUfjNksjBg/eefxhVDyf5+Hk4lC2SfPnuF//rXvyI8PBxDhgxBZGQkPvnkk76qioiIBimzsDh9UM/6JFl4/fXXkZWVhRUrVuDgwYP45S9/iZkzZ+LEiRN9UR0REQ1SFginD+pZnyQL+fn5WLBgAR5++GFMmDAB69atQ1hYGDZs2NAX1RER0SBl6YX/qGe9niy0tbWhuroaCQkJVucTEhKwd+/eTvEmkwlGo9HqICIiooGj15OF7777DmazGSEhIVbnQ0JCoNfrO8Xn5eVBo9HIB2+bJCIie5mFcPqgnvXZBMerZ8EKIbqcGbt8+XIYDAb5qK+v76smERHRNYZzFlyj12+dDAoKgqenZ6dehMbGxk69DQCgUqmgUql6uxlERETUS3q9Z8HHxweRkZEoLS21Ol9aWoqYmJjero6IiAYxCwTMThzsWbBPnyzKlJ2djdTUVEyZMgXR0dF46aWXcOLECfz2t7/ti+qIiGiQcnYogcmCffokWbj33ntx9uxZrFy5Eg0NDYiIiMCuXbswevTovqiOiIiI+lCfLfeckZGBjIyMvnp4IiIip+9o4N0Q9hlwe0MQUT9x4kPT0traL/USWf5zOFOeesbdN4iIiMgm9iwQEZHb6rirwZny1DMmC0RE5LbM4vLhTHnqGZMFIiJyW5yz4Bqcs0BEREQ2sWeBiIjclgUSzOi875Aj5alnTBaIiMhtWcTlw5ny1DMOQxAREZFN7FkgIiK3ZXZyGMKZsoMJkwUiInJbTBZcg8MQREREdhozZgwkSep0ZGZmAgCEEMjJyYFOp4NarUZcXBwOHz5s9RgmkwmLFi1CUFAQfH19kZycjJMnT1rFNDU1ITU1FRqNBhqNBqmpqTh//rxVzIkTJzB79mz4+voiKCgIixcvRltbm1XMoUOHEBsbC7VajZEjR2LlypUQCpZYZ7JARERuyyIkpw9HVFVVoaGhQT5KS0sBAPfccw8AYPXq1cjPz0dBQQGqqqqg1WoRHx+P5uZm+TGysrJQVFSEwsJCVFRU4MKFC0hKSoLZbJZjUlJSUFNTg+LiYhQXF6OmpgapqanydbPZjFmzZqGlpQUVFRUoLCzEm2++iSVLlsgxRqMR8fHx0Ol0qKqqwvr167FmzRrk5+c7/DpLQkmK0YeMRiM0Gg3icCe8JO/+bg4R2UNyoit3YH0EUS+4JNrxMd6GwWCAv79/n9TR8beirHYkhvkp/957odmC2IhvFbc1KysL7733Ho4dOwYA0Ol0yMrKwhNPPAHgci9CSEgInnvuOSxcuBAGgwEjRozAa6+9hnvvvRcAcOrUKYSFhWHXrl1ITEzEkSNHMHHiRFRWViIqKgoAUFlZiejoaHzxxRcYP3483n//fSQlJaG+vh46nQ4AUFhYiLS0NDQ2NsLf3x8bNmzA8uXLcfr0aahUKgDAqlWrsH79epw8eRKSA7+37FkgIqJBz2g0Wh0mk6nHMm1tbdi6dSseeughSJKEuro66PV6JCQkyDEqlQqxsbHYu3cvAKC6uhrt7e1WMTqdDhEREXLMvn37oNFo5EQBAKZOnQqNRmMVExERIScKAJCYmAiTyYTq6mo5JjY2Vk4UOmJOnTqF48ePO/T6MFkg9+DhqfygvieE8oP61jX+u2OGh9MHAISFhcnzAzQaDfLy8nqse8eOHTh//jzS0tIAAHq9HgAQEhJiFRcSEiJf0+v18PHxQUBAgM2Y4ODgTvUFBwdbxVxdT0BAAHx8fGzGdPzcEWMv3g1BRERuSyiYd3B1eQCor6+3Goa48tt4dzZt2oSZM2dafbsH0Kl7XwjRY5f/1TFdxfdGTMfMA0eGIAD2LBARkRvruHXSmQMA/P39rY6ekoVvvvkGu3fvxsMPPyyf02q1ADp/a29sbJS/0Wu1WrS1taGpqclmzOnTpzvVeebMGauYq+tpampCe3u7zZjGxkYAnXs/esJkgYiIyEGvvPIKgoODMWvWLPlceHg4tFqtfIcEcHleQ1lZGWJiYgAAkZGR8Pb2toppaGhAbW2tHBMdHQ2DwYADBw7IMfv374fBYLCKqa2tRUNDgxxTUlIClUqFyMhIOaa8vNzqdsqSkhLodDqMGTPGoefLZIGIiNyWWXg4fTjKYrHglVdewfz58+Hl9cNoviRJyMrKQm5uLoqKilBbW4u0tDQMHToUKSkpAACNRoMFCxZgyZIl2LNnDw4ePIh58+Zh0qRJmD59OgBgwoQJmDFjBtLT01FZWYnKykqkp6cjKSkJ48ePBwAkJCRg4sSJSE1NxcGDB7Fnzx4sXboU6enp8nBKSkoKVCoV0tLSUFtbi6KiIuTm5iI7O9vhYQjOWSAiIrdlgQSLE997LXB8ku3u3btx4sQJPPTQQ52uLVu2DK2trcjIyEBTUxOioqJQUlICPz8/OeaFF16Al5cX5s6di9bWVtxxxx3YvHkzPD1/mFS6bds2LF68WL5rIjk5GQUFBfJ1T09P7Ny5ExkZGZg2bRrUajVSUlKwZs0aOUaj0aC0tBSZmZmYMmUKAgICkJ2djezsbIefM9dZIPfgzMxsi7nnGKJrVT/87rhynYWdn/0Yvn7Kn2NLsxmzbvy6T9t6LWDPAhERuS3uDeEaTBaIiMhtKZ138EP5AdW5PmBxgiMRERHZxJ4FIiJyW5cnOCofSnCm7GDCZIGIiNyW5Yolm5WV5zCEPTgMQURERDaxZ4GIiNwWJzi6BpOFKzm4olWvGExvVCfu9/Yc5qu4rKX1ouKyor2t5yCigewaX2fEAg+XL8o0GDFZICIit2UWEsxO7DrpTNnBhHMWiIiIyCb2LBARkdsyO3k3hJnDEHZhskBERG7LIjxgcWKCo2UwzRtzAochiIiIyCb2LBARkdviMIRrMFkgIiK3ZYFzdzRYeq8p1zQOQxAREZFN7FkgIiK35fyiTPzObA8mC0RE5LacX+6ZyYI9+CoRERGRTexZICIit2WBBAucmeDI5Z7twWSBiIjcFochXIPJAhERuS3n11lgsmCPgZssSJKiLaMlHx/ldVoULs7hobwbS7Q5sQWyuy1T6sRWuc5sMy0NUSkuK8xObO/rblsDO7NFu7u9F53hxOvkoVL+XrSYTMoKDqZ/G+ozAzdZICIi6oFFSLA4sygTt6i2C5MFIiJyWxYnhyG4zoJ9+CoRERGRTexZICIit+X8FtX8zmwPJgtEROS2zJBgdmKtBGfKDiZMqYiIiMgm9iwQEZHb4jCEazBZICIit2WGc0MJbrYaSr9hSkVEREQ2sWeBiIjcFochXIPJAhERuS1uJOUaTBaIiMhtCSe3qBa8ddIuTKmIiIjIJvYsEBGR2+IwhGsM2GRB8vKGJHk7XM4zMEBxneZzTYrKObXNtBMkL+X/fOLSpV5siX2caa+H/zDFZS0XWhSXHUzbTEtejv++dRCX2hWXdbctlJ3ZZtojNER5xQ2nFRUTZoviKj0Cf6SsTksb0Ki4Wodw10nXYEpFRETkgG+//Rbz5s3D8OHDMXToUNx0002orq6WrwshkJOTA51OB7Vajbi4OBw+fNjqMUwmExYtWoSgoCD4+voiOTkZJ0+etIppampCamoqNBoNNBoNUlNTcf78eauYEydOYPbs2fD19UVQUBAWL16Mtqu+wB46dAixsbFQq9UYOXIkVq5cCeFgks5kgYiI3Jb5P1tUO3M4oqmpCdOmTYO3tzfef/99fP7551i7di1+9KMfyTGrV69Gfn4+CgoKUFVVBa1Wi/j4eDQ3N8sxWVlZKCoqQmFhISoqKnDhwgUkJSXBbP6hNzMlJQU1NTUoLi5GcXExampqkJqa+sNzN5sxa9YstLS0oKKiAoWFhXjzzTexZMkSOcZoNCI+Ph46nQ5VVVVYv3491qxZg/z8fIee94AdhiAiIupJbw1DGI1Gq/MqlQqqLoacnnvuOYSFheGVV16Rz40ZM0b+fyEE1q1bhxUrVmDOnDkAgC1btiAkJATbt2/HwoULYTAYsGnTJrz22muYPn06AGDr1q0ICwvD7t27kZiYiCNHjqC4uBiVlZWIiooCAGzcuBHR0dE4evQoxo8fj5KSEnz++eeor6+HTqcDAKxduxZpaWl49tln4e/vj23btuHixYvYvHkzVCoVIiIi8OWXXyI/Px/Z2dmQ7By67PWehZycHEiSZHVotdreroaIiKjXhIWFyd39Go0GeXl5Xca98847mDJlCu655x4EBwfj5ptvxsaNG+XrdXV10Ov1SEhIkM+pVCrExsZi7969AIDq6mq0t7dbxeh0OkRERMgx+/btg0ajkRMFAJg6dSo0Go1VTEREhJwoAEBiYiJMJpM8LLJv3z7ExsZaJT6JiYk4deoUjh8/bvfr0yc9CzfccAN2794t/+zp6dkX1RAR0SBngQcsTnzv7ShbX18Pf39/+XxXvQoA8PXXX2PDhg3Izs7Gk08+iQMHDmDx4sVQqVR48MEHodfrAQAhIdaTWUNCQvDNN98AAPR6PXx8fBAQENAppqO8Xq9HcHBwp/qDg4OtYq6uJyAgAD4+PlYxV/Z8XNk2vV6P8PDwbl4Za32SLHh5ebE3gYiI+pxZSDA7MQzRUdbf398qWeiOxWLBlClTkJubCwC4+eabcfjwYWzYsAEPPvigHHd1974Qoscu/6tjuorvjZiOyY32DkEAfTTB8dixY9DpdAgPD8d9992Hr7/+uttYk8kEo9FodRAREQ1EoaGhmDhxotW5CRMm4MSJEwAgf1Hu+GbfobGxUf5Gr9Vq0dbWhqamJpsxp093vl32zJkzVjFX19PU1IT29nabMY2Nl+9rvbpXwpZeTxaioqLw6quv4oMPPsDGjRuh1+sRExODs2fPdhmfl5dnNU4UFhbW200iIqJrVMcER2cOR0ybNg1Hjx61Ovfll19i9OjRAIDw8HBotVqUlpbK19va2lBWVoaYmBgAQGRkJLy9va1iGhoaUFtbK8dER0fDYDDgwIEDcsz+/fthMBisYmpra9HQ0CDHlJSUQKVSITIyUo4pLy+3up2ypKQEOp2u0/CELb2eLMycORO/+c1vMGnSJEyfPh07d+4EcHk2aFeWL18Og8EgH/X19b3dJCIiukaJ/+w6qfQQDq7g+Pjjj6OyshK5ubn46quvsH37drz00kvIzMwEcLlrPysrC7m5uSgqKkJtbS3S0tIwdOhQpKSkAAA0Gg0WLFiAJUuWYM+ePTh48CDmzZsn/90ELvdWzJgxA+np6aisrERlZSXS09ORlJSE8ePHAwASEhIwceJEpKam4uDBg9izZw+WLl2K9PR0eUglJSUFKpUKaWlpqK2tRVFREXJzcx26EwJwwa2Tvr6+mDRpEo4dO9bl9e5uTyEiIuqJGRLMTmwG5WjZW265BUVFRVi+fDlWrlyJ8PBwrFu3Dg888IAcs2zZMrS2tiIjIwNNTU2IiopCSUkJ/Pz85JgXXngBXl5emDt3LlpbW3HHHXdg8+bNVjcEbNu2DYsXL5bvmkhOTkZBQYF83dPTEzt37kRGRgamTZsGtVqNlJQUrFmzRo7RaDQoLS1FZmYmpkyZgoCAAGRnZyM7O9uh5y0JR5dxcpDJZMLYsWPxyCOP4I9//GOP8UajERqNBrd53wMvJcs9BwUqaSYAN1zu2Ym7TNxuuWdNzxOPuuPMcs/CZFJctl9wuec+5zFkiPKyTiz3bHGj5Z4vWdqwp/HvMBgMdk0aVKLjb8WCsrnwGab8vdt2oR2bYv+3T9t6Lej1noWlS5di9uzZGDVqFBobG/HMM8/AaDRi/vz5vV0VERENchbh3P4OFvfKVftNrycLJ0+exP3334/vvvsOI0aMwNSpU1FZWSlP/iAiIuotHXMPnClPPev1ZKGwsLC3H5KIiIj60YDdG0JcaoeSniXzd+ecqlNZQeX9WJIz2936ObFt83mD4rJK5zt4XDG5x1GmyfatMtYV1cHu1/noidnd5iw48V4cTPMOnGFx5j2hcN4BAFjalP37eDkxT8IQrexW9kvtF4Ediqt1iAUSLE5McHSm7GAyYJMFIiKinvTWCo5kGwdriIiIyCb2LBARkdviBEfXYLJARERuywLHl2y+ujz1jCkVERER2cSeBSIiclvCybshBHsW7MJkgYiI3JaSnSOvLk89Y7JARERuixMcXYOvEhEREdnEngUiInJbHIZwDSYLRETktrjcs2twGIKIiIhsYs8CERG5LQ5DuAaTBSIicltMFlxjwCYLkqcnJMnT4XLCbFZep4+PonKiXdmWzQAgSU68yZsvKC7bHywXWhSXVR060S/1Sl7Kf0WUbuXdb/prm2kPx3/PAcBDPURxlZbWi4rLwqL8M8ZyUXm9St+LQuHW1gCg2VevqNwli5tt7U49GrDJAhERUU/Ys+AaTBaIiMhtMVlwDd4NQURERDaxZ4GIiNyWgHNrJfTTTB23w2SBiIjcFochXIPJAhERuS0mC67BOQtERERkE3sWiIjIbbFnwTWYLBARkdtisuAaHIYgIiIim9izQEREbksICcKJ3gFnyg4mTBaIiMhtWSA5tc6CM2UHEw5DEBERkU0DtmfBw98PHh4KdoF0YgdIhAQpKiZOnVZcpeX77xWXlTyV7dYHAB4af8VlYVG45pmH8gxe8lUrL+vMa6xwJ1IAsDQ3Kyrn1G6VCndwvFyxRXlZSfn3Dg/focoKjg1TXKfnNw2Ky5rPn1dc1pmdPZXuqGtpanJ5nZeE8p0uHcUJjq4xYJMFIiKinnDOgmtwGIKIiIhsYs8CERG5LQ5DuAaTBSIiclschnANDkMQEZHbEv/pWVB6OJos5OTkQJIkq0Or1V7RHoGcnBzodDqo1WrExcXh8OHDVo9hMpmwaNEiBAUFwdfXF8nJyTh58qRVTFNTE1JTU6HRaKDRaJCamorzV02uPXHiBGbPng1fX18EBQVh8eLFaGtrs4o5dOgQYmNjoVarMXLkSKxcuRJCwURbJgtEREQOuOGGG9DQ0CAfhw4dkq+tXr0a+fn5KCgoQFVVFbRaLeLj49F8xd1RWVlZKCoqQmFhISoqKnDhwgUkJSXBfMXdJykpKaipqUFxcTGKi4tRU1OD1NRU+brZbMasWbPQ0tKCiooKFBYW4s0338SSJUvkGKPRiPj4eOh0OlRVVWH9+vVYs2YN8vPzHX7OHIYgIiK3JeDUHalQUtTLy8uqN0F+LCGwbt06rFixAnPmzAEAbNmyBSEhIdi+fTsWLlwIg8GATZs24bXXXsP06dMBAFu3bkVYWBh2796NxMREHDlyBMXFxaisrERUVBQAYOPGjYiOjsbRo0cxfvx4lJSU4PPPP0d9fT10Oh0AYO3atUhLS8Ozzz4Lf39/bNu2DRcvXsTmzZuhUqkQERGBL7/8Evn5+cjOzoYk2d+rwp4FIiJyWx0rODpzAJe/hV95mEymbus8duwYdDodwsPDcd999+Hrr78GANTV1UGv1yMhIUGOValUiI2Nxd69ewEA1dXVaG9vt4rR6XSIiIiQY/bt2weNRiMnCgAwdepUaDQaq5iIiAg5UQCAxMREmEwmVFdXyzGxsbFQqVRWMadOncLx48cdep2ZLBAR0aAXFhYmzw/QaDTIy8vrMi4qKgqvvvoqPvjgA2zcuBF6vR4xMTE4e/Ys9Ho9ACAkJMSqTEhIiHxNr9fDx8cHAQEBNmOCg4M71R0cHGwVc3U9AQEB8PHxsRnT8XNHjL04DEFERG6rt+6GqK+vh7//DyvbXvlt/EozZ86U/3/SpEmIjo7G2LFjsWXLFkydOhUAOnXvCyF67PK/Oqar+N6I6Zjc6MgQBMCeBSIicmPO3Alx5RoN/v7+Vkd3ycLVfH19MWnSJBw7dkyex3D1t/bGxkb5G71Wq0VbWxuarlqG++qY06c7byNw5swZq5ir62lqakJ7e7vNmMbGRgCdez96wmSBiIhIIZPJhCNHjiA0NBTh4eHQarUoLS2Vr7e1taGsrAwxMTEAgMjISHh7e1vFNDQ0oLa2Vo6Jjo6GwWDAgQMH5Jj9+/fDYDBYxdTW1qKh4Yd9TkpKSqBSqRAZGSnHlJeXW91OWVJSAp1OhzFjxjj0PJksEBGR2xLC+cMRS5cuRVlZGerq6rB//37cfffdMBqNmD9/PiRJQlZWFnJzc1FUVITa2lqkpaVh6NChSElJAQBoNBosWLAAS5YswZ49e3Dw4EHMmzcPkyZNku+OmDBhAmbMmIH09HRUVlaisrIS6enpSEpKwvjx4wEACQkJmDhxIlJTU3Hw4EHs2bMHS5cuRXp6ujyckpKSApVKhbS0NNTW1qKoqAi5ubkO3wkBcM4CERG5MVev4Hjy5Encf//9+O677zBixAhMnToVlZWVGD16NABg2bJlaG1tRUZGBpqamhAVFYWSkhL4+fnJj/HCCy/Ay8sLc+fORWtrK+644w5s3rwZnlfsJLxt2zYsXrxYvmsiOTkZBQUF8nVPT0/s3LkTGRkZmDZtGtRqNVJSUrBmzRo5RqPRoLS0FJmZmZgyZQoCAgKQnZ2N7Oxsh18nSShZyqkPGY1GaDQa3BEwH16Sgu2BvZTnP0KnbItqqeWi4jot9acUlxVObMftOTxQcdnWyDGKylm8nPiFdiKt9as62XNQN8yN3ykuKy4p26bXmW2xPYOGKy4rmi8oLiv5+/Uc1A1L03lldQ7zVVyn5O2tuKzS9gKApVX5Z4WkcIt3Sa18e3dxsfvbB225JNrxUfsbMBgMVpMGe1PH34qJhcvgOdS++QVdMX9vwuf3re7Ttl4L2LNARERui3tDuAaTBSIiclsWIUHirpN9jskCERG5LSWTFK8uTz3j3RBERERkE3sWiIjIbV3uWXBmzkIvNuYaxmSBiIjcFic4ugaHIYiIiMgm9iwQEZHbEv85nClPPWOyQEREbovDEK7BYQgiIiKyiT0LRETkvjgO4RJMFoiIyH05OQwBDkPYhckCERG5La7g6Bqcs0BEREQ2DdieBRGmhfB0fNtR4aU8/7H4ePYc1IWWnyrf1vRHF9sUl7Wca1JcFj9SvqXw+R8r29730lDFVWLYtxbFZU0/1SouqzKbFZcV/sMUl1WqPUh5nV5Nyrd8/n70jxSXVX+j7I0hmZRtAQ4AMCn/vfMIVraVPQB4OLFFNSRl3eVmrfJtyz31ZxWVE5Y2QK+4Wsfq4t0QLjFgkwUiIqIeCcm5eQdMFuzCYQgiIiKyiT0LRETktjjB0TUc7lkoLy/H7NmzodPpIEkSduzYYXVdCIGcnBzodDqo1WrExcXh8OHDvdVeIiKiH4heOKhHDicLLS0tmDx5MgoKCrq8vnr1auTn56OgoABVVVXQarWIj49Hc3Oz040lIiIi13N4GGLmzJmYOXNml9eEEFi3bh1WrFiBOXPmAAC2bNmCkJAQbN++HQsXLnSutURERFfg3RCu0asTHOvq6qDX65GQkCCfU6lUiI2Nxd69e7ssYzKZYDQarQ4iIiK7cQiiz/VqsqDXX76xNiQkxOp8SEiIfO1qeXl50Gg08hEWFtabTSIiIiIn9cmtk9JVi4cIITqd67B8+XIYDAb5qK+v74smERHRNahjGMKZg3rWq7dOarWXV8rT6/UIDQ2Vzzc2NnbqbeigUqmgUjm+UiMRERF3nXSNXu1ZCA8Ph1arRWlpqXyura0NZWVliImJ6c2qiIiIAEi9cFBPHO5ZuHDhAr766iv557q6OtTU1CAwMBCjRo1CVlYWcnNzMW7cOIwbNw65ubkYOnQoUlJSerXhRERE5BoOJwuffvopbrvtNvnn7OxsAMD8+fOxefNmLFu2DK2trcjIyEBTUxOioqJQUlICPz/lGxcRERF1icMQLuFwshAXFwdhY31MSZKQk5ODnJwcZ9pFRETUMyYLLjFg94aQ6vWQJB/HC44O7TmmG+cjlPV+DDupfLtbMUTBc/wPj+GBisteHBWguKzKoGy7aONPFVfp1Nbj301WPoF2SORYxWUv/ux7ReXEt2rFdQZ8rrgoVM1DFJf1aFf+iftdlLItny3KdkoHAAQddGJFWWc2E5CU97B6tJgUlfNsVL6VvWhR+B4Wyj8TaWAasMkCERFRj7hFtUswWSAiIrfFXSddo08WZSIiIqJrB3sWiIjIfXGCo0swWSAiIvfFOQsuwWEIIiIisok9C0RE5LYkcflwpjz1jMkCERG5L85ZcAkOQxARkfvqmLPgzOGEvLw8SJKErKysH5okBHJycqDT6aBWqxEXF4fDhw9blTOZTFi0aBGCgoLg6+uL5ORknDx50iqmqakJqamp0Gg00Gg0SE1Nxfnz561iTpw4gdmzZ8PX1xdBQUFYvHgx2tqsF8U6dOgQYmNjoVarMXLkSKxcudLmSsxdYbJARESkQFVVFV566SXceOONVudXr16N/Px8FBQUoKqqClqtFvHx8Whu/mHl0KysLBQVFaGwsBAVFRW4cOECkpKSYDab5ZiUlBTU1NSguLgYxcXFqKmpQWpqqnzdbDZj1qxZaGlpQUVFBQoLC/Hmm29iyZIlcozRaER8fDx0Oh2qqqqwfv16rFmzBvn5+Q49Vw5DEBGR++qnYYgLFy7ggQcewMaNG/HMM8/88HBCYN26dVixYgXmzJkDANiyZQtCQkKwfft2LFy4EAaDAZs2bcJrr72G6dOnAwC2bt2KsLAw7N69G4mJiThy5AiKi4tRWVmJqKgoAMDGjRsRHR2No0ePYvz48SgpKcHnn3+O+vp66HQ6AMDatWuRlpaGZ599Fv7+/ti2bRsuXryIzZs3Q6VSISIiAl9++SXy8/ORnZ0NSbKvZ4U9C0RE5L5ELxy4/A38ysNksr0XR2ZmJmbNmiX/se9QV1cHvV6PhIQE+ZxKpUJsbCz27t0LAKiurkZ7e7tVjE6nQ0REhByzb98+aDQaOVEAgKlTp0Kj0VjFREREyIkCACQmJsJkMqG6ulqOiY2NhUqlsoo5deoUjh8/bvM5XonJAhERDXphYWHy3ACNRoO8vLxuYwsLC/Gvf/2ryxi9Xg8ACAkJsTofEhIiX9Pr9fDx8UFAQIDNmODg4E6PHxwcbBVzdT0BAQHw8fGxGdPxc0eMPTgMQURE7quXhiHq6+vh7+8vn77ym/iV6uvr8dhjj6GkpARDhnS/U+vV3ftCiB67/K+O6Sq+N2I6JjfaOwQBDORkQZIAD8dnqUonTyuu0i9Q2dbAxjHKt5n2OTtUcVlLiPI9evU/V75ts6ZO2RbVQ8KUbwvc4qv8dQrUGhSXLbv5VcVlX28eo6jcgjj7s/2rHTC1Ky6bVv1fist6f+Lfc1A3Ar9Qtp2xM/fHXwxWvg24ZFZe8dAvlH8+waKsXjFM+e+O5K3sT4RkMQFGxdU6ppdWcPT397dKFrpTXV2NxsZGREZGyufMZjPKy8tRUFCAo0ePArj8rT00NFSOaWxslL/Ra7VatLW1oampyap3obGxETExMXLM6dOd3y9nzpyxepz9+/dbXW9qakJ7e7tVzNU9CI2NjQA6937YwmEIIiIiO91xxx04dOgQampq5GPKlCl44IEHUFNTgx//+MfQarUoLS2Vy7S1taGsrExOBCIjI+Ht7W0V09DQgNraWjkmOjoaBoMBBw4ckGP2798Pg8FgFVNbW4uGhgY5pqSkBCqVSk5moqOjUV5ebnU7ZUlJCXQ6HcaMGWP38x64PQtEREQ9cPUKjn5+foiIiLA65+vri+HDh8vns7KykJubi3HjxmHcuHHIzc3F0KFDkZKSAgDQaDRYsGABlixZguHDhyMwMBBLly7FpEmT5AmTEyZMwIwZM5Ceno4XX3wRAPDII48gKSkJ48ePBwAkJCRg4sSJSE1NxfPPP49z585h6dKlSE9Pl3tJUlJS8PTTTyMtLQ1PPvkkjh07htzcXPzxj3+8RoYhiIiIejIAV3BctmwZWltbkZGRgaamJkRFRaGkpAR+fn5yzAsvvAAvLy/MnTsXra2tuOOOO7B582Z4enrKMdu2bcPixYvluyaSk5NRUFAgX/f09MTOnTuRkZGBadOmQa1WIyUlBWvWrJFjNBoNSktLkZmZiSlTpiAgIADZ2dnIzs526DkxWSAiInLCxx9/bPWzJEnIyclBTk5Ot2WGDBmC9evXY/369d3GBAYGYuvWrTbrHjVqFN577z2bMZMmTUJ5ebnNmJ5wzgIRERHZxJ4FIiJyWxKcnLPQay25tjFZICIi99VLt06SbRyGICIiIpvYs0BERO5rAN4NcS1iskBERO6LyYJLcBiCiIiIbGLPAhERuS1Xr+A4WDFZICIi98VhCJcYsMnCpXEjAa/ut//sjtd3FxTX2TxK2e6RvqcvKa7Tolb+T9Dmr3zXyR/9P2U7RwLAsBPfKyp3vkajuM7hp5z5jR6uuOTPTQ8rLjvsPb+eg7qwZpTyW7kS7zzQc1A3vL3Mist6mpT/+5y7Xtnvnff3yuv80VcmxWWNo5Tv2DrkW1/FZS8FKNsp0/Oi8s8nz4ZWZQUtyt9LNDAN2GSBiIioR+xZcAkmC0RE5LY4Z8E1eDcEERER2cSeBSIicl9c7tklmCwQEZH74pwFl2CyQEREbotzFlyDcxaIiIjIJvYsEBGR++IwhEswWSAiIvfl5DAEkwX7cBiCiIiIbGLPAhERuS8OQ7gEkwUiInJfTBZcgsMQREREZBN7FoiIyG1xnQXXGLDJgtf5Vnh5KthG+dx5xXVqvlK2pfD5nyrbOhYA1N+2KC7r5eOpuOyQ7y4qLutxXlmbh9Ur35438N9GxWUtQ5Vv5T3iU+Vb7Ur6/6eoXGBYsOI6P98TobhsaJvy59ru16a4bOPPlG35POxb5VsvSxblfyGGVzcpLuthuKC4rHdbu7KCXso/J8zaAGXlzBeBbxVXSwMQhyGIiIjIpgHbs0BERNQjTnB0CSYLRETktjhnwTWYLBARkXvjH/w+xzkLREREZBN7FoiIyH1xzoJLMFkgIiK3xTkLrsFhCCIiIrKJPQtEROS+OAzhEkwWiIjIbXEYwjU4DEFEREQ2sWeBiIjcF4chXILJAhERuS8mCy7BYQgiIiI7bdiwATfeeCP8/f3h7++P6OhovP/++/J1IQRycnKg0+mgVqsRFxeHw4cPWz2GyWTCokWLEBQUBF9fXyQnJ+PkyZNWMU1NTUhNTYVGo4FGo0FqairOnz9vFXPixAnMnj0bvr6+CAoKwuLFi9HWZr0D7KFDhxAbGwu1Wo2RI0di5cqVEMLxDGnA9ixIbe2QPBzPZcQl5dvW+pw8p6hc8LeS4jrF962Ky/p8r2xLbQC4NEJ5WUnto6icRflO0fA0KN/KW/rqrOKylpbvldfroex94eHEe9jDrHybaUur8m3LfXyUvScAIKx+hKJy7TqN4jovBipv7zCDSXFZWCzKyyr4gAcAnDqtuEoPhVt5ewjlW5Y7ytUTHK+77jqsWrUKP/nJTwAAW7ZswZ133omDBw/ihhtuwOrVq5Gfn4/Nmzfjpz/9KZ555hnEx8fj6NGj8PO7/LmblZWFd999F4WFhRg+fDiWLFmCpKQkVFdXw9Pz8pbiKSkpOHnyJIqLiwEAjzzyCFJTU/Huu+8CAMxmM2bNmoURI0agoqICZ8+exfz58yGEwPr16wEARqMR8fHxuO2221BVVYUvv/wSaWlp8PX1xZIlSxx63gM2WSAiIupRLw1DGI1Gq9MqlQoqlapT+OzZs61+fvbZZ7FhwwZUVlZi4sSJWLduHVasWIE5c+YAuJxMhISEYPv27Vi4cCEMBgM2bdqE1157DdOnTwcAbN26FWFhYdi9ezcSExNx5MgRFBcXo7KyElFRUQCAjRs3Ijo6GkePHsX48eNRUlKCzz//HPX19dDpdACAtWvXIi0tDc8++yz8/f2xbds2XLx4EZs3b4ZKpUJERAS+/PJL5OfnIzs7G5Jk/xcaDkMQEZH7Er1wAAgLC5O7/DUaDfLy8nqs2mw2o7CwEC0tLYiOjkZdXR30ej0SEhLkGJVKhdjYWOzduxcAUF1djfb2dqsYnU6HiIgIOWbfvn3QaDRyogAAU6dOhUajsYqJiIiQEwUASExMhMlkQnV1tRwTGxtrlfQkJibi1KlTOH78eI/P70rsWSAiokGvvr4e/v7+8s9d9Sp0OHToEKKjo3Hx4kUMGzYMRUVFmDhxovyHPCQkxCo+JCQE33zzDQBAr9fDx8cHAQEBnWL0er0cExwc3Kne4OBgq5ir6wkICICPj49VzJgxYzrV03EtPDy82+d4NYd7FsrLyzF79mzodDpIkoQdO3ZYXU9LS4MkSVbH1KlTHa2GiIioRx1zFpw5AMgTFjsOW8nC+PHjUVNTg8rKSvzud7/D/Pnz8fnnn//Qpqu694UQPXb5Xx3TVXxvxHRMbnRkCAJQkCy0tLRg8uTJKCgo6DZmxowZaGhokI9du3Y5Wg0REVHPemkYwhE+Pj74yU9+gilTpiAvLw+TJ0/Gn//8Z2i1WgCQv9l3aGxslL/Ra7VatLW1oampyWbM6dOdJ6aeOXPGKubqepqamtDe3m4zprGxEUDn3o+eOJwszJw5E88884w8eaMrKpUKWq1WPgIDAx2thoiIyC0IIWAymRAeHg6tVovS0lL5WltbG8rKyhATEwMAiIyMhLe3t1VMQ0MDamtr5Zjo6GgYDAYcOHBAjtm/fz8MBoNVTG1tLRoaGuSYkpISqFQqREZGyjHl5eVWt1OWlJRAp9N1Gp7oSZ9McPz4448RHByMn/70p0hPT5czma6YTCYYjUarg4iIyB69NQxhryeffBKffPIJjh8/jkOHDmHFihX4+OOP8cADD0CSJGRlZSE3NxdFRUWora1FWloahg4dipSUFACARqPBggULsGTJEuzZswcHDx7EvHnzMGnSJPnuiAkTJmDGjBlIT09HZWUlKisrkZ6ejqSkJIwfPx4AkJCQgIkTJyI1NRUHDx7Enj17sHTpUqSnp8tzL1JSUqBSqZCWloba2loUFRUhNzfX4TshgD6Y4Dhz5kzcc889GD16NOrq6vCHP/wBt99+O6qrq7scA8rLy8PTTz/d280gIqLBwMUrOJ4+fRqpqaloaGiARqPBjTfeiOLiYsTHxwMAli1bhtbWVmRkZKCpqQlRUVEoKSmR11gAgBdeeAFeXl6YO3cuWltbcccdd2Dz5s3yGgsAsG3bNixevFi+ayI5Odlq+N/T0xM7d+5ERkYGpk2bBrVajZSUFKxZs0aO0Wg0KC0tRWZmJqZMmYKAgABkZ2cjOzvb4ZdJEkqWcuooLEkoKirCXXfd1W1MQ0MDRo8ejcLCwi6HLkwmE0ymHxY5MRqNCAsLw/TwRfDy6H6CSXfEuaaeg7oh/UjhIi8OZmhXcmZRJmj6Z1Emz2Zli/eciQroOagbIXsaeg7qhuWMmy3KpPHvOag7/bQok+TEokyS1s0WZaprVlzW48x5xWXF0CHKCjrx/ofCRZkuiTbsMW6FwWCwusOgNxmNRmg0GkzIzIWnSuFrA8Bsuogjf3myT9t6LejzWydDQ0MxevRoHDt2rMvr3S18QURE1CPuDeESfZ4snD17FvX19QgNDe3rqoiIaJCR/nM4U5565nCycOHCBXz11Vfyz3V1daipqUFgYCACAwORk5OD3/zmNwgNDcXx48fx5JNPIigoCL/+9a97teFERETkGg4nC59++iluu+02+eeOiRLz58/Hhg0bcOjQIbz66qs4f/48QkNDcdttt+H111+3mtxBRETUKzgM4RIOJwtxcXE2t7f84IMPnGoQERGRvVy96+RgNWD3hrA0fgeL5PiMZWFyYvvYFmV3Jniolc/EFWN0PQd1x4m7MLxPfKe4rLigbLvokHPKZ5GLCxcUl3XmjgZYlN9dAG+FE3eHK79rRPgo3wdc+vqE4rKWZuX/tlD47+P9nfKZ6z4Byu+kEM3Kt0sXCu+QAQCcVvY7K9qUbxdtUfh5ahbtiut0GHsWXIK7ThIREZFNA7ZngYiIyC7sHehzTBaIiMhtcc6Ca3AYgoiIiGxizwIREbkvTnB0CSYLRETktjgM4RochiAiIiKb2LNARETui8MQLsFkgYiI3BaHIVyDwxBERERkE3sWiIjIfXEYwiWYLBARkftisuASTBaIiMhtcc6Ca3DOAhEREdk0YHsWJJUKkoeLt6gWFmXlnNh2VjrRoLgsLMpTYvP3yrdtFmaF2zY3NSmu05ntuCH656uDaL+krNxJ5e8JyUv5r7RFYXudpnAbcPP584qrlJzYUtvDz09x2fYbRisu633slKJyHv7K2yvOKNsWWxIS4MRHsUM4DOESAzZZICIi6okkBCQnvhA4U3Yw4TAEERER2cSeBSIicl8chnAJJgtEROS2eDeEa3AYgoiIiGxizwIREbkvDkO4BJMFIiJyWxyGcA0OQxAREZFN7FkgIiL3xWEIl2CyQEREbovDEK7BZIGIiNwXexZcgnMWiIiIyCb2LBARkVvjUELfG7DJgsXYDIvk7XA54cROjB4+jtcHAJbWi4rrFG1tisv2126K/aK/nqsTu11K3sp+vSytrYrrhKS8s9BDPURxWcU7kQKKd53sr/eExYkdK71r65TX26LwfeHErrhKP5+EaFdcp4LKnHsvDKbPUSdwGIKIiIhsGrA9C0RERD3h3RCuwZ4FIiJyX6IXDgfk5eXhlltugZ+fH4KDg3HXXXfh6NGj1k0SAjk5OdDpdFCr1YiLi8Phw4etYkwmExYtWoSgoCD4+voiOTkZJ0+etIppampCamoqNBoNNBoNUlNTcf78eauYEydOYPbs2fD19UVQUBAWL16MtquGjw4dOoTY2Fio1WqMHDkSK1euhHBw+IXJAhERkZ3KysqQmZmJyspKlJaW4tKlS0hISEBLS4scs3r1auTn56OgoABVVVXQarWIj49H8xXzXbKyslBUVITCwkJUVFTgwoULSEpKgvmK+T8pKSmoqalBcXExiouLUVNTg9TUVPm62WzGrFmz0NLSgoqKChQWFuLNN9/EkiVL5Bij0Yj4+HjodDpUVVVh/fr1WLNmDfLz8x163pJwNL3oY0ajERqNBrd5/QZebjLB0ZmXkBMcBzhnJjj6+Cgq59R7op8mODozyVfxBEcnSF79MwLrMcxXcVl3muB4SbTjY7EDBoMB/v7+iuu3peNvxS2/fgZe3srfu5faL6Kq6PeK23rmzBkEBwejrKwMt956K4QQ0Ol0yMrKwhNPPAHgci9CSEgInnvuOSxcuBAGgwEjRozAa6+9hnvvvRcAcOrUKYSFhWHXrl1ITEzEkSNHMHHiRFRWViIqKgoAUFlZiejoaHzxxRcYP3483n//fSQlJaG+vh46nQ4AUFhYiLS0NDQ2NsLf3x8bNmzA8uXLcfr0aahUKgDAqlWrsH79epw8eRKSnZ9x7FkgIiL31UvDEEaj0eowmUx2VW8wGAAAgYGBAIC6ujro9XokJCTIMSqVCrGxsdi7dy8AoLq6Gu3t7VYxOp0OERERcsy+ffug0WjkRAEApk6dCo1GYxUTEREhJwoAkJiYCJPJhOrqajkmNjZWThQ6Yk6dOoXjx4/b9RwBJgtEREQICwuT5wZoNBrk5eX1WEYIgezsbPziF79AREQEAECv1wMAQkJCrGJDQkLka3q9Hj4+PggICLAZExwc3KnO4OBgq5ir6wkICICPj4/NmI6fO2LswbshiIjIbfXW3RD19fVWwxBXfhPvzqOPPorPPvsMFRUVnR/3qu59IUSPXf5Xx3QV3xsxHUPn9g5BAOxZICIid9axKJMzBwB/f3+ro6dkYdGiRXjnnXfw0Ucf4brrrpPPa7VaAJ2/tTc2Nsrf6LVaLdra2tDU1GQz5vTp053qPXPmjFXM1fU0NTWhvb3dZkxjYyOAzr0ftjBZICIit9XRs+DM4QghBB599FG89dZb+PDDDxEeHm51PTw8HFqtFqWlpfK5trY2lJWVISYmBgAQGRkJb29vq5iGhgbU1tbKMdHR0TAYDDhw4IAcs3//fhgMBquY2tpaNDQ0yDElJSVQqVSIjIyUY8rLy61upywpKYFOp8OYMWPsft5MFoiIiOyUmZmJrVu3Yvv27fDz84Ner4der0frf5ZplyQJWVlZyM3NRVFREWpra5GWloahQ4ciJSUFAKDRaLBgwQIsWbIEe/bswcGDBzFv3jxMmjQJ06dPBwBMmDABM2bMQHp6OiorK1FZWYn09HQkJSVh/PjxAICEhARMnDgRqampOHjwIPbs2YOlS5ciPT1dHlJJSUmBSqVCWloaamtrUVRUhNzcXGRnZzs0DME5C0RE5L5cvEX1hg0bAABxcXFW51955RWkpaUBAJYtW4bW1lZkZGSgqakJUVFRKCkpgZ+fnxz/wgsvwMvLC3PnzkVrayvuuOMObN68GZ6ennLMtm3bsHjxYvmuieTkZBQUFMjXPT09sXPnTmRkZGDatGlQq9VISUnBmjVr5BiNRoPS0lJkZmZiypQpCAgIQHZ2NrKzsx163lxn4QpcZ4E64ToLduE6C/bhOgu9p+NvxdRZf3J6nYXKnX/o07ZeCzgMQURERDYN2GEIcekShBPf6pSwmCzKCvIb/rWrP3qNnNpuV/m3dMv33ztRbz/8Dnh49hzTXVFnvuFfaOk5qBvm8wbFZd2KK98P3KLaJQZsskBERNQT7jrpGhyGICIiIpvYs0BERO7LxXdDDFZMFoiIyG1xGMI1OAxBRERENrFngYiI3JdFXD6cKU89YrJARETui3MWXILJAhERuS0JTs5Z6LWWXNs4Z4GIiIhsYs8CERG5L67g6BJMFoiIyG3x1knX4DAEERER2cSeBSIicl+8G8IlmCwQEZHbkoSA5MS8A2fKDiZMFq7ENw31Jnd7P7lbey3Kt+M2Gy/0S739QfJS/jEvLl3qxZaQO2OyQERE7svyn8OZ8tQjJgtEROS2OAzhGrwbgoiIiGxyKFnIy8vDLbfcAj8/PwQHB+Ouu+7C0aNHrWKEEMjJyYFOp4NarUZcXBwOHz7cq40mIiIC8MPdEM4c1COHkoWysjJkZmaisrISpaWluHTpEhISEtDS0iLHrF69Gvn5+SgoKEBVVRW0Wi3i4+PR3Nzc640nIqJBrmMFR2cO6pFDcxaKi4utfn7llVcQHByM6upq3HrrrRBCYN26dVixYgXmzJkDANiyZQtCQkKwfft2LFy4sPdaTkREgx5XcHQNp+YsGAwGAEBgYCAAoK6uDnq9HgkJCXKMSqVCbGws9u7d2+VjmEwmGI1Gq4OIiIgGDsXJghAC2dnZ+MUvfoGIiAgAgF6vBwCEhIRYxYaEhMjXrpaXlweNRiMfYWFhSptERESDDYchXEJxsvDoo4/is88+wz/+8Y9O1yTJeodwIUSncx2WL18Og8EgH/X19UqbREREg4xkcf6gnilaZ2HRokV45513UF5ejuuuu04+r9VqAVzuYQgNDZXPNzY2dupt6KBSqaBSqZQ0g4iIiFzAoZ4FIQQeffRRvPXWW/jwww8RHh5udT08PBxarRalpaXyuba2NpSVlSEmJqZ3WkxERNSBwxAu4VDPQmZmJrZv3463334bfn5+8jwEjUYDtVoNSZKQlZWF3NxcjBs3DuPGjUNubi6GDh2KlJSUPnkCREQ0iHHXSZdwKFnYsGEDACAuLs7q/CuvvIK0tDQAwLJly9Da2oqMjAw0NTUhKioKJSUl8PPz65UGExERkWs5lCwIO7prJElCTk4OcnJylLaJiIjILtwbwjW4kVQvcGYLWA8nelwsTqyKya1nqZNu7liyi7t94LrZNtPOuOZ/152dd+Bu791+wo2kiIiIyCb2LBARkfsSAJxZK4EdC3ZhskBERG6LcxZcg8kCERG5LwEn5yz0WkuuaZyzQERERDaxZ4GIiNwX74ZwCfYsEBGR+7L0wuGg8vJyzJ49GzqdDpIkYceOHVbXhRDIycmBTqeDWq1GXFwcDh8+bBVjMpmwaNEiBAUFwdfXF8nJyTh58qRVTFNTE1JTU+VdmVNTU3H+/HmrmBMnTmD27Nnw9fVFUFAQFi9ejLa2NquYQ4cOITY2Fmq1GiNHjsTKlSvtWjfpSkwWiIiIHNDS0oLJkyejoKCgy+urV69Gfn4+CgoKUFVVBa1Wi/j4eDRfsTZOVlYWioqKUFhYiIqKCly4cAFJSUkwm39YAyQlJQU1NTUoLi5GcXExampqkJqaKl83m82YNWsWWlpaUFFRgcLCQrz55ptYsmSJHGM0GhEfHw+dToeqqiqsX78ea9asQX5+vkPPmcMQRETktvrjboiZM2di5syZXV4TQmDdunVYsWIF5syZAwDYsmULQkJCsH37dixcuBAGgwGbNm3Ca6+9hunTpwMAtm7dirCwMOzevRuJiYk4cuQIiouLUVlZiaioKADAxo0bER0djaNHj2L8+PEoKSnB559/jvr6euh0OgDA2rVrkZaWhmeffRb+/v7Ytm0bLl68iM2bN0OlUiEiIgJffvkl8vPzkZ2dDcnOxdjYs0BERO6rl3adNBqNVofJZFLUnLq6Ouj1eiQkJMjnVCoVYmNjsXfvXgBAdXU12tvbrWJ0Oh0iIiLkmH379kGj0ciJAgBMnToVGo3GKiYiIkJOFAAgMTERJpMJ1dXVckxsbCxUKpVVzKlTp3D8+HG7nxeTBSIiGvTCwsLkuQEajQZ5eXmKHqdjN+aQkBCr8yEhIfI1vV4PHx8fBAQE2IwJDg7u9PjBwcFWMVfXExAQAB8fH5sxHT93xNiDwxBEROS+euluiPr6evj7+8unr/wmrsTV3ftCiB67/K+O6Sq+N2I6JjfaOwQBsGeBiIjcWS8NQ/j7+1sdSpMFrVYLoPO39sbGRvkbvVarRVtbG5qammzGnD59utPjnzlzxirm6nqamprQ3t5uM6axsRFA594PW5gsEBER9ZLw8HBotVqUlpbK59ra2lBWVoaYmBgAQGRkJLy9va1iGhoaUFtbK8dER0fDYDDgwIEDcsz+/fthMBisYmpra9HQ0CDHlJSUQKVSITIyUo4pLy+3up2ypKQEOp0OY8aMsft5cRiiFzizzXT7jWMUl/X+7LjisuarMlq6RjixzbSHE92uFoWTwQBwURxyjgWAE7urK1ln4cKFC/jqq6/kn+vq6lBTU4PAwECMGjUKWVlZyM3Nxbhx4zBu3Djk5uZi6NChSElJAQBoNBosWLAAS5YswfDhwxEYGIilS5di0qRJ8t0REyZMwIwZM5Ceno4XX3wRAPDII48gKSkJ48ePBwAkJCRg4sSJSE1NxfPPP49z585h6dKlSE9Pl4dUUlJS8PTTTyMtLQ1PPvkkjh07htzcXPzxj390aBiCyQIREbmt/rh18tNPP8Vtt90m/5ydnQ0AmD9/PjZv3oxly5ahtbUVGRkZaGpqQlRUFEpKSuB3xRfLF154AV5eXpg7dy5aW1txxx13YPPmzfD09JRjtm3bhsWLF8t3TSQnJ1ut7eDp6YmdO3ciIyMD06ZNg1qtRkpKCtasWSPHaDQalJaWIjMzE1OmTEFAQACys7PlNttLEo4u49THjEYjNBoN4nAnvCTv/m6OXTyvmtHqCPYsUK9izwINAJdEOz7G2zAYDFaTBntTx9+K6eMeh5en8vfuJbMJu4+90KdtvRZwzgIRERHZxGEIIiJyXxYBSE70TlnYs2UPJgtEROS+uOukS3AYgoiIiGxizwIREbkxJ3sWwJ4FezBZICIi98VhCJfgMAQRERHZxJ4FIiJyXxYBp4YSeDeEXZgsEBGR+xKWy4cz5alHHIYgIiIim9izQERE7osTHF2CycKVPDx7jumCpblZcZXO7O/gTL10jXLig4/7O1AnCj8TISyKdnNUhHMWXILJAhERuS/2LLgE5ywQERGRTexZICIi9yXgZM9Cr7XkmsZkgYiI3BeHIVyCwxBERERkE3sWiIjIfVkscOrWCwsXZbIHkwUiInJfHIZwCQ5DEBERkU3sWSAiIvfFngWXYLJARETuiys4ugSHIYiIiMgm9iwQEZHbEsIC4cQ2086UHUyYLBARkfsSwrmhBM5ZsAuTBSIicl/CyTkLTBbscu0lC0q3VAXg6T9MUTmz8YLiOs1NTYrLEvUqfmhem5z4TPRQD1FWTngALYqrpQHo2ksWiIho8LBYAMmJeQecs2AXJgtEROS+OAzhErx1koiIiGxizwIREbktYbFAODEMwVsn7cNkgYiI3BeHIVyCwxBERERkE3sWiIjIfVkEILFnoa8xWSAiIvclBABnbp1ksmAPDkMQERGRTexZICIityUsAsKJYQjBngW7sGeBiIjcl7A4fyjw17/+FeHh4RgyZAgiIyPxySef9PITG1iYLBARkdsSFuH04ajXX38dWVlZWLFiBQ4ePIhf/vKXmDlzJk6cONEHz3BgYLJARETkgPz8fCxYsAAPP/wwJkyYgHXr1iEsLAwbNmzo76b1mQE3Z6Fj/OgS2pWts+HEalxCtCkqZxbtiuuEMCsvS0TUEyc+Ez2Esu+Tl/7zmeiK+QCXhMmp53gJl9tqNBqtzqtUKqhUqk7xbW1tqK6uxv/8z/9YnU9ISMDevXsVt2OgG3DJQnNzMwCgAruUPYAzK3eed6IsEdFA5MxnopPbTDc3N0Oj0Tj3IN3w8fGBVqtFhV7h34orDBs2DGFhYVbnnnrqKeTk5HSK/e6772A2mxESEmJ1PiQkBHq93um2DFQDLlnQ6XSor6+Hn58fJEnqdN1oNCIsLAz19fXw9/fvhxa6B75O9uHrZB++Tvbh63SZEALNzc3Q6XR9VseQIUNQV1eHtjZlPcJXEkJ0+nvTVa/Cla6O7+oxriUDLlnw8PDAdddd12Ocv7//oP5ltBdfJ/vwdbIPXyf78HVCn/UoXGnIkCEYMmRIn9dzpaCgIHh6enbqRWhsbOzU23At4QRHIiIiO/n4+CAyMhKlpaVW50tLSxETE9NPrep7A65ngYiIaCDLzs5GamoqpkyZgujoaLz00ks4ceIEfvvb3/Z30/qM2yULKpUKTz31VI/jSYMdXyf78HWyD18n+/B1GhzuvfdenD17FitXrkRDQwMiIiKwa9cujB49ur+b1mckwbUuiYiIyAbOWSAiIiKbmCwQERGRTUwWiIiIyCYmC0RERGQTkwUiIiKyya2ShcG2f7ijcnJyIEmS1aHVavu7Wf2uvLwcs2fPhk6ngyRJ2LFjh9V1IQRycnKg0+mgVqsRFxeHw4cP909j+1FPr1NaWlqn99fUqVP7p7H9KC8vD7fccgv8/PwQHByMu+66C0ePHrWK4XuKrjVukywMxv3DlbjhhhvQ0NAgH4cOHervJvW7lpYWTJ48GQUFBV1eX716NfLz81FQUICqqipotVrEx8fLm5oNFj29TgAwY8YMq/fXrl3Ob+LjbsrKypCZmYnKykqUlpbi0qVLSEhIQEvLD7su8T1F1xzhJn7+85+L3/72t1bnrr/+evE///M//dSigeepp54SkydP7u9mDGgARFFRkfyzxWIRWq1WrFq1Sj538eJFodFoxN/+9rd+aOHAcPXrJIQQ8+fPF3feeWe/tGcga2xsFABEWVmZEILvKbo2uUXPQsf+4QkJCVbnr/X9w5U4duwYdDodwsPDcd999+Hrr7/u7yYNaHV1ddDr9VbvLZVKhdjYWL63uvDxxx8jODgYP/3pT5Geno7Gxsb+blK/MxgMAIDAwEAAfE/RtcktkoXBun+4o6KiovDqq6/igw8+wMaNG6HX6xETE4OzZ8/2d9MGrI73D99bPZs5cya2bduGDz/8EGvXrkVVVRVuv/12mEym/m5avxFCIDs7G7/4xS8QEREBgO8puja51d4Qg23/cEfNnDlT/v9JkyYhOjoaY8eOxZYtW5Cdnd2PLRv4+N7q2b333iv/f0REBKZMmYLRo0dj586dmDNnTj+2rP88+uij+Oyzz1BRUdHpGt9TdC1xi56Fwbp/uLN8fX0xadIkHDt2rL+bMmB13C3C95bjQkNDMXr06EH7/lq0aBHeeecdfPTRR7juuuvk83xP0bXILZKFwbp/uLNMJhOOHDmC0NDQ/m7KgBUeHg6tVmv13mpra0NZWRnfWz04e/Ys6uvrB937SwiBRx99FG+99RY+/PBDhIeHW13ne4quRW4zDDEY9w931NKlSzF79myMGjUKjY2NeOaZZ2A0GjF//vz+blq/unDhAr766iv557q6OtTU1CAwMBCjRo1CVlYWcnNzMW7cOIwbNw65ubkYOnQoUlJS+rHVrmfrdQoMDEROTg5+85vfIDQ0FMePH8eTTz6JoKAg/PrXv+7HVrteZmYmtm/fjrfffht+fn5yD4JGo4FarYYkSXxP0bWnX+/FcNBf/vIXMXr0aOHj4yN+9rOfybcq0WX33nuvCA0NFd7e3kKn04k5c+aIw4cP93ez+t1HH30kAHQ65s+fL4S4fKvbU089JbRarVCpVOLWW28Vhw4d6t9G9wNbr9P3338vEhISxIgRI4S3t7cYNWqUmD9/vjhx4kR/N9vlunqNAIhXXnlFjuF7iq41khBCuD5FISIiInfhFnMWiIiIqP8wWSAiIiKbmCwQERGRTUwWiIiIyCYmC0RERGQTkwUiIiKyickCERER2cRkgYiIiGxiskBEREQ2MVkgIiIim5gsEBERkU3/H/9lW1rK1r/GAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "for filter in curves:\n", @@ -597,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -607,29 +264,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[np.str_('SLOAN/SDSS.uprime_filter'),\n", - " np.str_('SLOAN/SDSS.u'),\n", - " np.str_('SLOAN/SDSS.g'),\n", - " np.str_('SLOAN/SDSS.gprime_filter'),\n", - " np.str_('SLOAN/SDSS.r'),\n", - " np.str_('SLOAN/SDSS.rprime_filter'),\n", - " np.str_('SLOAN/SDSS.i'),\n", - " np.str_('SLOAN/SDSS.iprime_filter'),\n", - " np.str_('SLOAN/SDSS.z'),\n", - " np.str_('SLOAN/SDSS.zprime_filter')]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "filters" @@ -637,110 +274,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "for i,name in zip(images, filters):\n", @@ -759,30 +295,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "# Create an RGB image\n", diff --git a/notebooks/psf.ipynb b/notebooks/psf.ipynb index 7b408935..f80b2a79 100644 --- a/notebooks/psf.ipynb +++ b/notebooks/psf.ipynb @@ -86,7 +86,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" - } }, "nbformat": 4, diff --git a/notebooks/rubix_pipeline_sharding.py b/notebooks/rubix_pipeline_sharding.py index b9734973..cfbbe6cd 100644 --- a/notebooks/rubix_pipeline_sharding.py +++ b/notebooks/rubix_pipeline_sharding.py @@ -1,31 +1,31 @@ import os -#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3' +# os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3' # Specify the number of GPUs to use -#os.environ['CUDA_VISIBLE_DEVICES'] = "1,4,5,8,9" +# os.environ['CUDA_VISIBLE_DEVICES'] = "1,4,5,8,9" -#os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false" +# os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false" -#Set the FSPS path to the template files +# Set the FSPS path to the template files # os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps' -#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps' -#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps' -#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps' -os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps' +# os.environ['SPS_HOME'] = '/home/annalena/sps_fsps' +# os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps' +# os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps' +os.environ["SPS_HOME"] = "/home/annalena_data/sps_fsps" import jax import jax.numpy as jnp import matplotlib.pyplot as plt -from rubix.core.pipeline import RubixPipeline + +from rubix.core.pipeline import RubixPipeline + # Now JAX will list two CpuDevice entries print(jax.devices()) - config = { - "pipeline":{"name": "calc_ifu"}, - + "pipeline": {"name": "calc_ifu"}, "logger": { "log_level": "DEBUG", "log_file_path": None, @@ -40,12 +40,10 @@ "snapshot": 99, "save_data_path": "data", }, - "load_galaxy_args": { - "id": 14, - "reuse": True, + "id": 14, + "reuse": True, }, - "subset": { "use_subset": True, "subset_size": 10000, @@ -56,35 +54,29 @@ "args": { "path": "data/galaxy-id-14.hdf5", }, - }, "output_path": "output", - - "telescope": - {"name": "MUSE", - "psf": {"name": "gaussian", "size": 5, "sigma": 0.6}, - "lsf": {"sigma": 0.5}, - "noise": {"signal_to_noise": 100,"noise_distribution": "normal"},}, - "cosmology": - {"name": "PLANCK15"}, - - "galaxy": - {"dist_z": 0.1, - "rotation": {"type": "edge-on"}, - }, - + "telescope": { + "name": "MUSE", + "psf": {"name": "gaussian", "size": 5, "sigma": 0.6}, + "lsf": {"sigma": 0.5}, + "noise": {"signal_to_noise": 100, "noise_distribution": "normal"}, + }, + "cosmology": {"name": "PLANCK15"}, + "galaxy": { + "dist_z": 0.1, + "rotation": {"type": "edge-on"}, + }, "ssp": { - "template": { - "name": "FSPS" - }, + "template": {"name": "FSPS"}, "dust": { - "extinction_model": "Cardelli89", - "dust_to_gas_ratio": 0.01, - "dust_to_metals_ratio": 0.4, - "dust_grain_density": 3.5, - "Rv": 3.1, - }, - }, + "extinction_model": "Cardelli89", + "dust_to_gas_ratio": 0.01, + "dust_to_metals_ratio": 0.4, + "dust_grain_density": 3.5, + "Rv": 3.1, + }, + }, } pipe = RubixPipeline(config) @@ -92,27 +84,27 @@ rubixdata = pipe.run_sharded(inputdata) -#Plotting the spectra +# Plotting the spectra wave = pipe.telescope.wave_seq plt.figure(figsize=(10, 5)) plt.title("Spectra of a single star") plt.xlabel("Wavelength (Angstroms)") plt.ylabel("Luminosity") -#spectra = rubixdata.stars.datacube # Spectra of all stars +# spectra = rubixdata.stars.datacube # Spectra of all stars spectra = rubixdata -plt.plot(wave, spectra[12,12,:]) -plt.plot(wave, spectra[12,14,:]) +plt.plot(wave, spectra[12, 12, :]) +plt.plot(wave, spectra[12, 14, :]) plt.savefig("./output/rubix_spectra.jpg") plt.close() plt.figure(figsize=(6, 5)) # get the indices of the visible wavelengths of 4000-8000 Angstroms visible_indices = jnp.where((wave >= 4000) & (wave <= 8000)) -#visible_spectra = rubixdata.stars.datacube[:, :, visible_indices[0]] +# visible_spectra = rubixdata.stars.datacube[:, :, visible_indices[0]] visible_spectra = rubixdata[:, :, visible_indices[0]] # Sum up all spectra to create an image -image = jnp.sum(visible_spectra, axis = 2) +image = jnp.sum(visible_spectra, axis=2) plt.imshow(image, origin="lower", cmap="inferno") plt.colorbar() plt.title("Image of the galaxy") @@ -120,5 +112,3 @@ plt.ylabel("Y pixel") plt.savefig("./output/rubix_image.jpg") plt.close() - - diff --git a/notebooks/rubix_pipeline_single_function.ipynb b/notebooks/rubix_pipeline_single_function.ipynb index 9832ed3b..dce63a4a 100644 --- a/notebooks/rubix_pipeline_single_function.ipynb +++ b/notebooks/rubix_pipeline_single_function.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -60,27 +60,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'module' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m#NBVAL_SKIP\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcore\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RubixPipeline \n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mos\u001b[39;00m\n\u001b[32m 5\u001b[39m config = {\n\u001b[32m 6\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mpipeline\u001b[39m\u001b[33m\"\u001b[39m:{\u001b[33m\"\u001b[39m\u001b[33mname\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mcalc_ifu\u001b[39m\u001b[33m\"\u001b[39m},\n\u001b[32m 7\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 66\u001b[39m }, \n\u001b[32m 67\u001b[39m }\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/core/pipeline.py:28\u001b[39m\n\u001b[32m 25\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m linear_pipeline \u001b[38;5;28;01mas\u001b[39;00m pipeline\n\u001b[32m 26\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_config, get_pipeline_config\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_reshape_data, get_rubix_data\n\u001b[32m 29\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdust\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_extinction\n\u001b[32m 30\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mifu\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 31\u001b[39m get_calculate_datacube,\n\u001b[32m 32\u001b[39m get_calculate_spectra,\n\u001b[32m 33\u001b[39m get_doppler_shift_and_resampling,\n\u001b[32m 34\u001b[39m get_scale_spectrum_by_mass,\n\u001b[32m 35\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/core/data.py:13\u001b[39m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mbeartype\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m beartype \u001b[38;5;28;01mas\u001b[39;00m typechecker\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjaxtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m jaxtyped\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgalaxy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m IllustrisAPI, get_input_handler\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mgalaxy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01malignment\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m center_particles\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mlogger\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_logger\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/galaxy/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01minput_handler\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m 2\u001b[39m IllustrisHandler,\n\u001b[32m 3\u001b[39m BaseHandler,\n\u001b[32m 4\u001b[39m IllustrisAPI,\n\u001b[32m 5\u001b[39m get_input_handler,\n\u001b[32m 6\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/galaxy/input_handler/__init__.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01millustris\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m IllustrisHandler\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BaseHandler\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01millustris_api\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m IllustrisAPI\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/galaxy/input_handler/illustris.py:9\u001b[39m\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m convert_values_to_physical, SFTtoAge\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrubix\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m \u001b[38;5;28;43;01mclass\u001b[39;49;00m\u001b[38;5;250;43m \u001b[39;49m\u001b[34;43;01mIllustrisHandler\u001b[39;49;00m\u001b[43m(\u001b[49m\u001b[43mBaseHandler\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 10\u001b[39m \u001b[38;5;250;43m \u001b[39;49m\u001b[33;43;03m\"\"\"\u001b[39;49;00m\n\u001b[32m 11\u001b[39m \u001b[33;43;03m This class is used to handle the input data from the Illustris simulation.\u001b[39;49;00m\n\u001b[32m 12\u001b[39m \u001b[33;43;03m The data is stored in HDF5 files, which are read using the h5py library.\u001b[39;49;00m\n\u001b[32m 13\u001b[39m \u001b[33;43;03m The data is then converted to physical units using the values in the header of the file.\u001b[39;49;00m\n\u001b[32m 14\u001b[39m \u001b[33;43;03m The data is then stored in a dictionary, which can be accessed using the get_particle_data() method.\u001b[39;49;00m\n\u001b[32m 15\u001b[39m \u001b[33;43;03m \"\"\"\u001b[39;49;00m\n\u001b[32m 17\u001b[39m \u001b[43m \u001b[49m\u001b[43mMAPPED_FIELDS\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mIllustrisHandler\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mMAPPED_FIELDS\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/rubix/rubix/galaxy/input_handler/illustris.py:17\u001b[39m, in \u001b[36mIllustrisHandler\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mIllustrisHandler\u001b[39;00m(BaseHandler):\n\u001b[32m 10\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 11\u001b[39m \u001b[33;03m This class is used to handle the input data from the Illustris simulation.\u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[33;03m The data is stored in HDF5 files, which are read using the h5py library.\u001b[39;00m\n\u001b[32m 13\u001b[39m \u001b[33;03m The data is then converted to physical units using the values in the header of the file.\u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[33;03m The data is then stored in a dictionary, which can be accessed using the get_particle_data() method.\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m MAPPED_FIELDS = \u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mIllustrisHandler\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[33m\"\u001b[39m\u001b[33mMAPPED_FIELDS\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# This Dictionary maps the particle name in the simulation to the name used in Rubix\u001b[39;00m\n\u001b[32m 19\u001b[39m MAPPED_PARTICLE_KEYS = config[\u001b[33m\"\u001b[39m\u001b[33mIllustrisHandler\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mMAPPED_PARTICLE_KEYS\u001b[39m\u001b[33m\"\u001b[39m]\n", - "\u001b[31mTypeError\u001b[39m: 'module' object is not subscriptable" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -239,65 +221,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 20:56:48,004 - rubix - INFO - Getting rubix data...\n", - "2025-05-20 20:56:48,005 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-05-20 20:56:48,084 - rubix - INFO - Centering stars particles\n", - "2025-05-20 20:56:49,283 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100000 for stars\n", - "2025-05-20 20:56:49,284 - rubix - INFO - Data loaded with 100000 star particles and 0 gas particles.\n", - "2025-05-20 20:56:49,285 - rubix - INFO - Setting up the pipeline...\n", - "2025-05-20 20:56:49,285 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'reshape_data': {'name': 'reshape_data', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'reshape_data', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-05-20 20:56:49,286 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-05-20 20:56:49,288 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 20:56:49,299 - rubix - INFO - Getting cosmology...\n", - "2025-05-20 20:56:49,440 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-05-20 20:56:49,450 - rubix - INFO - Getting cosmology...\n", - "2025-05-20 20:56:49,476 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "2025-05-20 20:56:49,522 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 20:56:49,579 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 20:56:49,592 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 20:56:49,792 - rubix - INFO - Assembling the pipeline...\n", - "2025-05-20 20:56:49,793 - rubix - INFO - Compiling the expressions...\n", - "2025-05-20 20:56:49,794 - rubix - INFO - Running the pipeline on the input data...\n", - "2025-05-20 20:56:49,795 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-05-20 20:56:49,796 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", - "2025-05-20 20:56:49,796 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", - "2025-05-20 20:56:49,856 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-05-20 20:56:49,861 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-05-20 20:56:49,878 - rubix - WARNING - Attribute value of datacube is None or not an array\n", - "2025-05-20 20:56:49,882 - rubix - WARNING - Attribute value of spectra is None or not an array\n", - "2025-05-20 20:56:49,883 - rubix - WARNING - Attribute value of tree_flatten is None or not an array\n", - "2025-05-20 20:56:49,883 - rubix - WARNING - Attribute value of tree_unflatten is None or not an array\n", - "2025-05-20 20:56:49,884 - rubix - INFO - Calculating IFU cube...\n", - "2025-05-20 20:56:49,884 - rubix - DEBUG - Input shapes: Metallicity: 1, Age: 1\n", - "2025-05-20 20:56:49,989 - rubix - DEBUG - Calculation Finished! Spectra shape: (100000, 5994)\n", - "2025-05-20 20:56:49,991 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-05-20 20:56:49,998 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-05-20 20:56:49,999 - rubix - DEBUG - Doppler Shifted SSP Wave: (1, 100000, 5994)\n", - "2025-05-20 20:56:49,999 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-05-20 20:56:50,312 - rubix - INFO - Calculating Data Cube...\n", - "2025-05-20 20:56:50,315 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-05-20 20:56:50,316 - rubix - INFO - Convolving with PSF...\n", - "2025-05-20 20:56:50,320 - rubix - INFO - Convolving with LSF...\n", - "2025-05-20 20:56:50,326 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-05-20 20:57:19,641 - rubix - INFO - Pipeline run completed in 30.36 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config)\n", @@ -309,19 +233,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'RubixPipeline' object has no attribute 'run_sharded'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m rubixdata_2 = \u001b[43mpipe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_sharded\u001b[49m()\n", - "\u001b[31mAttributeError\u001b[39m: 'RubixPipeline' object has no attribute 'run_sharded'" - ] - } - ], + "outputs": [], "source": [ "rubixdata_2 = pipe.run_sharded()" ] @@ -360,35 +272,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(25, 25, 3721)\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -413,28 +297,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index e6fb4c78..d94f201b 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -20,17 +20,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -52,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -113,26 +105,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-20 22:00:18,377 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-05-20 22:00:18,378 - rubix - INFO - Rubix version: 0.0.post430+g5b4f76d.d20250520\n", - "2025-05-20 22:00:18,379 - rubix - INFO - JAX version: 0.6.0\n", - "2025-05-20 22:00:18,379 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -199,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -354,18 +329,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_NIHAO)" @@ -373,103 +339,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-20 22:00:19,477 - rubix - INFO - Getting rubix data...\n", - "2025-05-20 22:00:19,478 - rubix - INFO - Loading data into input handler\n", - "2025-05-20 22:00:19,478 - rubix - INFO - Using PynbodyHandler to load a NIHAO galaxy\n", - "2025-05-20 22:00:19,481 - rubix - INFO - Galaxy redshift (dist_z) set to: 0.1\n", - "2025-05-20 22:00:19,494 - rubix - INFO - Simulation snapshot loaded from halo 0\n", - "2025-05-20 22:00:19,572 - rubix - INFO - Halo data loaded.\n", - "2025-05-20 22:00:22,039 - rubix - WARNING - Field 'sfr' -> 'sfr' not found for gas. Assigning zeros.\n", - "2025-05-20 22:00:22,040 - rubix - WARNING - Field 'internal_energy' -> 'u' not found for gas. Assigning zeros.\n", - "2025-05-20 22:00:22,041 - rubix - WARNING - Field 'electron_abundance' -> 'electron_abundance' not found for gas. Assigning zeros.\n", - "2025-05-20 22:00:22,066 - rubix - INFO - Metals assigned to gas particles.\n", - "2025-05-20 22:00:22,066 - rubix - INFO - Metals shape is: (155341, 10)\n", - "2025-05-20 22:00:22,067 - rubix - INFO - Simulation snapshot and halo data loaded successfully for classes: ['stars', 'gas'].\n", - "2025-05-20 22:00:22,067 - rubix - DEBUG - Converting to Rubix format..\n", - "2025-05-20 22:00:22,132 - rubix - INFO - Half-mass radius calculated: 1.45 kpc\n", - "2025-05-20 22:00:22,133 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-05-20 22:00:22,134 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", - "2025-05-20 22:00:22,136 - rubix - DEBUG - Converting redshift for galaxy data into \n", - "2025-05-20 22:00:22,137 - rubix - DEBUG - Converting center for galaxy data into kpc\n", - "2025-05-20 22:00:22,138 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", - "2025-05-20 22:00:22,138 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", - "2025-05-20 22:00:22,141 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", - "2025-05-20 22:00:22,143 - rubix - DEBUG - Converting metallicity for particle type stars into \n", - "2025-05-20 22:00:22,146 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", - "2025-05-20 22:00:22,153 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", - "2025-05-20 22:00:22,160 - rubix - DEBUG - Converting density for particle type gas into Msun/kpc^3\n", - "2025-05-20 22:00:22,162 - rubix - DEBUG - Converting temperature for particle type gas into K\n", - "2025-05-20 22:00:22,163 - rubix - DEBUG - Converting metals for particle type gas into \n", - "2025-05-20 22:00:22,165 - rubix - DEBUG - Converting metallicity for particle type gas into \n", - "2025-05-20 22:00:22,166 - rubix - DEBUG - Converting coords for particle type gas into kpc\n", - "2025-05-20 22:00:22,167 - rubix - DEBUG - Converting velocity for particle type gas into km/s\n", - "2025-05-20 22:00:22,168 - rubix - DEBUG - Converting mass for particle type gas into Msun\n", - "2025-05-20 22:00:22,169 - rubix - DEBUG - Converting sfr for particle type gas into Msun/yr\n", - "2025-05-20 22:00:22,170 - rubix - DEBUG - Converting internal_energy for particle type gas into erg/g\n", - "2025-05-20 22:00:22,171 - rubix - DEBUG - Converting electron_abundance for particle type gas into \n", - "2025-05-20 22:00:22,172 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-05-20 22:00:22,222 - rubix - INFO - Centering stars particles\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Converted to Rubix format!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-20 22:00:22,552 - rubix - INFO - Data loaded with 1043618 star particles and 0 gas particles.\n", - "2025-05-20 22:00:22,553 - rubix - INFO - Setting up the pipeline...\n", - "2025-05-20 22:00:22,553 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-05-20 22:00:22,553 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-05-20 22:00:22,555 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 22:00:23,326 - rubix - INFO - Getting cosmology...\n", - "2025-05-20 22:00:23,463 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-05-20 22:00:23,471 - rubix - INFO - Getting cosmology...\n", - "2025-05-20 22:00:23,920 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 22:00:24,399 - rubix - DEBUG - SSP Wave: (5333,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 22:00:24,410 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-20 22:00:24,545 - rubix - INFO - Assembling the pipeline...\n", - "2025-05-20 22:00:24,545 - rubix - INFO - Compiling the expressions...\n", - "2025-05-20 22:00:24,546 - rubix - INFO - Number of devices: 1\n", - "2025-05-20 22:00:24,628 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-05-20 22:00:24,730 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-05-20 22:00:24,734 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-05-20 22:00:24,760 - rubix - INFO - Calculating IFU cube...\n", - "2025-05-20 22:00:24,760 - rubix - DEBUG - Input shapes: Metallicity: 1043618, Age: 1043618\n", - "2025-05-20 22:00:24,980 - rubix - DEBUG - Calculation Finished! Spectra shape: (1043618, 5333)\n", - "2025-05-20 22:00:24,981 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-05-20 22:00:24,985 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-05-20 22:00:24,986 - rubix - DEBUG - Doppler Shifted SSP Wave: (1043618, 5333)\n", - "2025-05-20 22:00:24,986 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-05-20 22:00:25,053 - rubix - INFO - Calculating Data Cube...\n", - "2025-05-20 22:00:25,054 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-05-20 22:00:25,055 - rubix - INFO - Convolving with PSF...\n", - "2025-05-20 22:00:25,058 - rubix - INFO - Convolving with LSF...\n", - "2025-05-20 22:00:25,063 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-05-20 22:00:27,011 - rubix - INFO - Pipeline run completed in 4.46 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -479,108 +351,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[-3.1244421e+03 -2.3109651e+03 -2.0055480e+03 ... -1.2120083e+03\n", - " -1.3375792e+03 -7.9277850e+02]\n", - " [-3.8948296e+03 -9.9477026e+02 9.6987311e+02 ... -4.2732915e+03\n", - " -4.2401475e+03 -3.9022600e+03]\n", - " [-3.6310061e+03 -1.2855078e+03 -1.1210460e+03 ... -1.1222546e+03\n", - " -1.0423126e+03 -9.7859808e+02]\n", - " ...\n", - " [-2.7880623e+03 -1.9734590e+03 -5.0688306e+02 ... -3.1797351e+03\n", - " -3.4406570e+03 -3.4141396e+03]\n", - " [-1.9713757e+03 -1.9807825e+03 -9.0053284e+02 ... -4.1801104e+03\n", - " -4.3672441e+03 -4.1323481e+03]\n", - " [ 4.9658901e+01 -5.7049971e+03 -6.4394551e+03 ... -2.7122344e+03\n", - " -2.4240444e+03 -2.2186223e+03]]\n", - "\n", - " [[-6.1910991e+03 -6.1451768e+03 -6.2967378e+03 ... -2.3543020e+03\n", - " -2.4165703e+03 -2.4096201e+03]\n", - " [-5.3934741e+03 -5.0396533e+03 -4.5310410e+03 ... -4.8921401e+03\n", - " -4.7913770e+03 -4.7794272e+03]\n", - " [-7.0857314e+03 -6.5290640e+03 -6.1235781e+03 ... -5.0580137e+03\n", - " -5.0012476e+03 -4.9760371e+03]\n", - " ...\n", - " [-6.4192046e+03 -5.3413877e+03 -5.0410649e+03 ... -4.3669092e+03\n", - " -4.9825347e+03 -5.0488794e+03]\n", - " [-5.7569917e+03 -5.2280347e+03 -4.5223115e+03 ... -5.4029375e+03\n", - " -5.5996426e+03 -5.7261963e+03]\n", - " [-3.8117400e+03 -5.0391382e+03 -3.8952700e+03 ... -3.9984387e+03\n", - " -3.6006785e+03 -3.1596533e+03]]\n", - "\n", - " [[-2.8416853e+03 -2.3564866e+03 -2.5248916e+03 ... -3.7183334e+02\n", - " -3.1459370e+00 -1.0692181e+02]\n", - " [-6.5656338e+03 -6.4775581e+03 -5.6938682e+03 ... -5.5669688e+03\n", - " -5.1034219e+03 -5.2947744e+03]\n", - " [-1.3856588e+04 -1.2909471e+04 -9.1808018e+03 ... -8.2766953e+03\n", - " -8.3049180e+03 -8.3014551e+03]\n", - " ...\n", - " [-5.4218481e+03 -2.6428242e+03 -1.6904259e+03 ... -7.4597451e+03\n", - " -7.7844126e+03 -7.0464937e+03]\n", - " [-8.6163838e+03 -8.4821436e+03 -6.3044990e+03 ... -7.2356836e+03\n", - " -6.9350132e+03 -6.9669761e+03]\n", - " [-1.1756783e+04 -1.0178306e+04 -3.8007595e+03 ... -2.9617896e+03\n", - " -2.1269763e+03 -2.1435381e+03]]\n", - "\n", - " ...\n", - "\n", - " [[-7.6463018e+03 -7.3724849e+03 -5.0412974e+03 ... -1.1776318e+03\n", - " -8.6093146e+02 -5.8580035e+02]\n", - " [-5.6333184e+03 -5.2926797e+03 -2.7025679e+03 ... -2.5581853e+03\n", - " -2.2468369e+03 -1.8670671e+03]\n", - " [-1.1069069e+04 -1.1135386e+04 -4.2830317e+03 ... -2.4913025e+03\n", - " -2.1093999e+03 -1.8686008e+03]\n", - " ...\n", - " [-4.6334365e+03 -3.5985349e+03 -3.2936924e+03 ... -7.7681763e+03\n", - " -7.5709106e+03 -7.4167061e+03]\n", - " [-3.4164858e+03 -3.0404700e+03 -2.3577202e+03 ... -3.5729558e+03\n", - " -3.7026521e+03 -4.4680610e+03]\n", - " [-6.1268130e+03 -5.1308936e+03 -3.7307917e+03 ... -3.6545771e+03\n", - " -2.2534617e+03 -3.9874753e+03]]\n", - "\n", - " [[-7.5809678e+03 -7.9255532e+03 -7.5749058e+03 ... -2.1790430e+02\n", - " 1.8591484e+02 1.4235336e+01]\n", - " [-5.0077798e+03 -5.1975386e+03 -5.0613848e+03 ... -2.0941709e+03\n", - " -1.9097306e+03 -1.9657228e+03]\n", - " [-8.2713984e+03 -8.3808643e+03 -6.4268003e+03 ... -2.9995337e+03\n", - " -3.0931309e+03 -2.9606821e+03]\n", - " ...\n", - " [-5.5525200e+03 -5.0606250e+03 -5.3721377e+03 ... -5.3280259e+03\n", - " -5.7883247e+03 -5.8715464e+03]\n", - " [ 7.1304602e+02 2.3150601e+03 2.5620811e+03 ... -1.5811039e+03\n", - " -1.3280884e+03 -2.0840212e+03]\n", - " [-3.7228977e+03 -1.9496025e+03 -7.4770042e+01 ... -4.8236005e+02\n", - " 2.0743861e+02 -1.3905100e+03]]\n", - "\n", - " [[-6.2210464e+03 -6.6607651e+03 -6.7526450e+03 ... -2.2686143e+03\n", - " -2.0451918e+03 -2.2248467e+03]\n", - " [-4.4195381e+03 -4.3444565e+03 -4.2108809e+03 ... -3.6484170e+03\n", - " -3.4013362e+03 -3.6047097e+03]\n", - " [-4.5340830e+03 -2.9486379e+03 -1.7724464e+03 ... -3.7968474e+03\n", - " -3.9168552e+03 -3.5546072e+03]\n", - " ...\n", - " [-2.4467217e+03 -2.2195771e+03 -2.7039868e+03 ... -2.4849395e+03\n", - " -2.5109910e+03 -2.2456633e+03]\n", - " [ 6.7643542e+02 1.0970240e+03 4.8213348e+02 ... -1.4007925e+03\n", - " -1.2085845e+03 -1.1107657e+03]\n", - " [-1.5701598e+03 -1.0600278e+03 -5.9062360e+02 ... -7.4803308e+02\n", - " -5.0066211e+02 -6.3199823e+02]]]\n" - ] - } - ], + "outputs": [], "source": [ "print(rubixdata)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -601,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -622,20 +402,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -671,20 +440,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb index a9a91ca8..0560da96 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -12,17 +12,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -44,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -105,26 +97,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-26 11:52:39,915 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-05-26 11:52:39,916 - rubix - INFO - Rubix version: 0.0.post431+gbb5adbd.d20250526\n", - "2025-05-26 11:52:39,916 - rubix - INFO - JAX version: 0.6.0\n", - "2025-05-26 11:52:39,917 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -191,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -346,18 +321,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -365,100 +331,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-26 11:52:41,325 - rubix - INFO - Getting rubix data...\n", - "2025-05-26 11:52:41,326 - rubix - INFO - Loading data from IllustrisAPI\n", - "2025-05-26 11:52:41,327 - rubix - DEBUG - Loading galaxy with ID 12\n", - "2025-05-26 11:52:41,327 - rubix - DEBUG - Performing GET request from http://www.tng-project.org/api/TNG50-1/snapshots/99/subhalos/12/cutout.hdf5?stars=Coordinates,GFM_InitialMass,GFM_Metallicity,GFM_StellarFormationTime,Velocities, with parameters None\n", - "2025-05-26 11:52:43,269 - rubix - DEBUG - Performing GET request from http://www.tng-project.org/api/TNG50-1/snapshots/99/subhalos/12, with parameters None\n", - "2025-05-26 11:52:43,456 - rubix - DEBUG - Appending subhalo data for subhalo 12\n", - "2025-05-26 11:52:43,500 - rubix - INFO - Loading data into input handler\n", - "2025-05-26 11:52:43,500 - rubix - DEBUG - Loading data from Illustris file..\n", - "2025-05-26 11:52:43,501 - rubix - DEBUG - Checking if the fields are present in the file...\n", - "2025-05-26 11:52:43,501 - rubix - DEBUG - Keys in the file: \n", - "2025-05-26 11:52:43,502 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", - "2025-05-26 11:52:43,502 - rubix - DEBUG - Matching fields: {'SubhaloData', 'Header', 'PartType4'}\n", - "2025-05-26 11:52:43,506 - rubix - DEBUG - Found 649384 valid particles out of 649384\n", - "2025-05-26 11:52:43,977 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", - "2025-05-26 11:52:54,030 - rubix - DEBUG - Converting to Rubix format..\n", - "2025-05-26 11:52:54,032 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", - "2025-05-26 11:52:54,032 - rubix - DEBUG - Keys in the particle data: dict_keys(['stars'])\n", - "2025-05-26 11:52:54,033 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", - "2025-05-26 11:52:54,033 - rubix - DEBUG - Matching fields: {'stars'}\n", - "2025-05-26 11:52:54,034 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", - "2025-05-26 11:52:54,034 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", - "2025-05-26 11:52:54,035 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-05-26 11:52:54,036 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", - "2025-05-26 11:52:54,040 - rubix - DEBUG - Converting redshift for galaxy data into \n", - "2025-05-26 11:52:54,041 - rubix - DEBUG - Converting center for galaxy data into kpc\n", - "2025-05-26 11:52:54,042 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", - "2025-05-26 11:52:54,043 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", - "2025-05-26 11:52:54,052 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", - "2025-05-26 11:52:54,075 - rubix - DEBUG - Converting metallicity for particle type stars into \n", - "2025-05-26 11:52:54,077 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", - "2025-05-26 11:52:54,079 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", - "2025-05-26 11:52:54,086 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-05-26 11:52:54,120 - rubix - INFO - Centering stars particles\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Converted to Rubix format!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-26 11:52:54,556 - rubix - INFO - Data loaded with 649384 star particles and 0 gas particles.\n", - "2025-05-26 11:52:54,558 - rubix - INFO - Setting up the pipeline...\n", - "2025-05-26 11:52:54,558 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-05-26 11:52:54,559 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-05-26 11:52:54,561 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-26 11:52:54,926 - rubix - INFO - Getting cosmology...\n", - "2025-05-26 11:52:55,125 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-05-26 11:52:55,137 - rubix - INFO - Getting cosmology...\n", - "2025-05-26 11:52:55,723 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-26 11:52:56,333 - rubix - DEBUG - SSP Wave: (5333,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-26 11:52:56,349 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-26 11:52:56,556 - rubix - INFO - Assembling the pipeline...\n", - "2025-05-26 11:52:56,557 - rubix - INFO - Compiling the expressions...\n", - "2025-05-26 11:52:56,558 - rubix - INFO - Number of devices: 1\n", - "2025-05-26 11:52:56,664 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-05-26 11:52:56,804 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-05-26 11:52:56,811 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-05-26 11:52:56,846 - rubix - INFO - Calculating IFU cube...\n", - "2025-05-26 11:52:56,847 - rubix - DEBUG - Input shapes: Metallicity: 649384, Age: 649384\n", - "2025-05-26 11:52:57,157 - rubix - DEBUG - Calculation Finished! Spectra shape: (649384, 5333)\n", - "2025-05-26 11:52:57,158 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-05-26 11:52:57,164 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-05-26 11:52:57,165 - rubix - DEBUG - Doppler Shifted SSP Wave: (649384, 5333)\n", - "2025-05-26 11:52:57,165 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-05-26 11:52:57,257 - rubix - INFO - Calculating Data Cube...\n", - "2025-05-26 11:52:57,260 - rubix - DEBUG - Datacube Shape: (300, 300, 3721)\n", - "2025-05-26 11:52:57,261 - rubix - INFO - Convolving with PSF...\n", - "2025-05-26 11:52:57,265 - rubix - INFO - Convolving with LSF...\n", - "2025-05-26 11:52:57,271 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-05-26 11:53:00,094 - rubix - INFO - Pipeline run completed in 5.54 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -468,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -484,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -507,19 +382,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-26 12:02:55,333 - rubix - INFO - Datacube saved to ./output/NIHAO_idg7.66e11_snap1024_subsetFalse.fits\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "from rubix.core.fits import store_fits\n", @@ -545,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -566,20 +431,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -615,20 +469,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import numpy as np\n", diff --git a/notebooks/rubix_pipeline_stepwise.ipynb b/notebooks/rubix_pipeline_stepwise.ipynb index 38910d83..e8db48e3 100644 --- a/notebooks/rubix_pipeline_stepwise.ipynb +++ b/notebooks/rubix_pipeline_stepwise.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -130,29 +130,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-05 13:48:57,361 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-03-05 13:48:57,361 - rubix - INFO - Rubix version: 0.0.post366+g4480c14\n", - "2025-03-05 13:48:57,361 - rubix - INFO - JAX version: 0.5.0\n", - "2025-03-05 13:48:57,378 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n", - "2025-03-05 13:48:57,378 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-03-05 13:48:57,555 - rubix - INFO - Centering stars particles\n", - "2025-03-05 13:48:58,286 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.core.data import convert_to_rubix, prepare_input\n", @@ -170,30 +150,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -212,18 +171,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-05 13:48:58,860 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-03-05 13:48:58,864 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.core.rotation import get_galaxy_rotation\n", @@ -234,30 +184,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#NBVAL_SKIP\n", "# Make a scatter plot of the stars coordinates after rotation\n", @@ -275,21 +204,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-05 13:49:00,335 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-03-05 13:49:00,656 - rubix - INFO - Getting cosmology...\n", - "2025-03-05 13:49:00,810 - rubix - INFO - Filtering particles outside the aperture...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.core.telescope import get_filter_particles\n", @@ -309,23 +226,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-03-05 13:49:00,950 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:24: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-03-05 13:49:00,959 - rubix - INFO - Getting cosmology...\n", - "2025-03-05 13:49:00,961 - rubix - INFO - Assigning particles to spaxels...\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.core.telescope import get_spaxel_assignment\n", @@ -345,25 +248,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-05 13:49:01,159 - rubix - WARNING - Attribute value of age_unit is None or not an array\n", - "2025-03-05 13:49:01,173 - rubix - WARNING - Attribute value of coords_unit is None or not an array\n", - "2025-03-05 13:49:01,173 - rubix - WARNING - Attribute value of datacube is None or not an array\n", - "2025-03-05 13:49:01,221 - rubix - WARNING - Attribute value of mass_unit is None or not an array\n", - "2025-03-05 13:49:01,222 - rubix - WARNING - Attribute value of metallicity_unit is None or not an array\n", - "2025-03-05 13:49:01,295 - rubix - WARNING - Attribute value of spectra is None or not an array\n", - "2025-03-05 13:49:01,296 - rubix - WARNING - Attribute value of tree_flatten is None or not an array\n", - "2025-03-05 13:49:01,296 - rubix - WARNING - Attribute value of tree_unflatten is None or not an array\n", - "2025-03-05 13:49:01,297 - rubix - WARNING - Attribute value of velocity_unit is None or not an array\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.core.data import get_reshape_data\n", @@ -383,27 +270,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "You need to have the SPS_HOME environment variable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[9], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# NBVAL_SKIP\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mifu\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_calculate_spectra\n\u001b[1;32m 3\u001b[0m calcultae_spectra \u001b[38;5;241m=\u001b[39m get_calculate_spectra(config)\n\u001b[1;32m 5\u001b[0m rubixdata \u001b[38;5;241m=\u001b[39m calcultae_spectra(rubixdata)\n", - "File \u001b[0;32m~/Documents/GitHub/rubix/rubix/core/ifu.py:16\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspectra\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mifu\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 10\u001b[0m cosmological_doppler_shift,\n\u001b[1;32m 11\u001b[0m resample_spectrum,\n\u001b[1;32m 12\u001b[0m velocity_doppler_shift,\n\u001b[1;32m 13\u001b[0m calculate_cube,\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RubixData\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mssp\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_lookup_interpolation_pmap, get_ssp\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtelescope\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_telescope\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m RubixData\n", - "File \u001b[0;32m~/Documents/GitHub/rubix/rubix/core/ssp.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mjax\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlogger\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_logger\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspectra\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mssp\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfactory\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_ssp_template\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Callable\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mjaxtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m jaxtyped\n", - "File \u001b[0;32m~/Documents/GitHub/rubix/rubix/spectra/ssp/factory.py:3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m read_yaml\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspectra\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mssp\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgrid\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SSPGrid, HDF5SSPGrid, pyPipe3DSSPGrid\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mspectra\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mssp\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfsps_grid\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m write_fsps_data_to_disk\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m config \u001b[38;5;28;01mas\u001b[39;00m rubix_config\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrubix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpaths\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TEMPLATE_PATH\n", - "File \u001b[0;32m~/Documents/GitHub/rubix/rubix/spectra/ssp/fsps_grid.py:20\u001b[0m\n\u001b[1;32m 18\u001b[0m HAS_FSPS \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mutil\u001b[38;5;241m.\u001b[39mfind_spec(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfsps\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m HAS_FSPS:\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mfsps\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 22\u001b[0m logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 24\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/rubix/lib/python3.12/site-packages/fsps/__init__.py:4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Check the that SPS_HOME variable is set properly\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mfsps\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msps_home\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m check_sps_home\n\u001b[0;32m----> 4\u001b[0m \u001b[43mcheck_sps_home\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m check_sps_home\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# End check\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/rubix/lib/python3.12/site-packages/fsps/sps_home.py:8\u001b[0m, in \u001b[0;36mcheck_sps_home\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcheck_sps_home\u001b[39m():\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSPS_HOME\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39menviron:\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou need to have the SPS_HOME environment variable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 10\u001b[0m path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSPS_HOME\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(path):\n", - "\u001b[0;31mRuntimeError\u001b[0m: You need to have the SPS_HOME environment variable" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# NBVAL_SKIP\n", "from rubix.core.ifu import get_calculate_spectra\n", diff --git a/notebooks/ssp_template.ipynb b/notebooks/ssp_template.ipynb index d0370568..cf0a1426 100644 --- a/notebooks/ssp_template.ipynb +++ b/notebooks/ssp_template.ipynb @@ -11,266 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-07-16 11:48:20,669 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> < \n", - "/_/|_|\\____/____/___/_/|_| \n", - " \n", - "\n", - "2024-07-16 11:48:20,671 - rubix - INFO - Rubix version: 0.0.post66+g42d5801.d20240712\n", - "2024-07-16 11:48:20,671 - rubix - WARNING - python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables.\n" - ] - }, - { - "data": { - "text/plain": [ - "HDF5SSPGrid(age=Array([ 0. , 5.100002 , 5.1500006, 5.1999993, 5.25 ,\n", - " 5.3000016, 5.350002 , 5.4000006, 5.4500012, 5.500002 ,\n", - " 5.550002 , 5.600002 , 5.6500025, 5.700002 , 5.750002 ,\n", - " 5.8000026, 5.850003 , 5.900003 , 5.950003 , 6. ,\n", - " 6.0200005, 6.040001 , 6.0599985, 6.0799985, 6.100002 ,\n", - " 6.120001 , 6.1399984, 6.16 , 6.18 , 6.1999993,\n", - " 6.2200007, 6.24 , 6.2599998, 6.2799997, 6.2999997,\n", - " 6.3199987, 6.3399997, 6.3600006, 6.3799996, 6.3999987,\n", - " 6.4200006, 6.44 , 6.4599996, 6.4799995, 6.499999 ,\n", - " 6.52 , 6.539999 , 6.56 , 6.5799994, 6.6 ,\n", - " 6.6199994, 6.6399994, 6.66 , 6.679999 , 6.699999 ,\n", - " 6.72 , 6.7399993, 6.7599993, 6.7799997, 6.799999 ,\n", - " 6.819999 , 6.839999 , 6.8599997, 6.879999 , 6.899999 ,\n", - " 6.919999 , 6.939999 , 6.959999 , 6.9799986, 6.999999 ,\n", - " 7.0200005, 7.040001 , 7.0599985, 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8.706548 8.756548 8.806548\n", - " 8.856548 8.9065485 8.956549 9.006547 9.05655 9.106548\n", - " 9.156549 9.206551 9.225309 9.230449 9.255273 9.278753\n", - " 9.30103 9.322219 9.3424225 9.361728 9.380211 9.39794\n", - " 9.414973 9.439333 9.477121 9.511884 9.544068 9.574031\n", - " 9.60206 9.628389 9.653213 9.676694 9.69897 9.72016\n", - " 9.740363 9.759667 9.7781515 9.79588 9.812913 9.829304\n", - " 9.8450985 9.860338 9.875061 9.889301 9.90309 9.916454\n", - " 9.929419 9.942008 9.954243 9.966142 9.977724 9.989004\n", - " 10. 10.010724 10.02119 10.031408 10.041392 10.051152\n", - " 10.060698 10.070038 10.079182 10.088136 10.09691 10.10551\n", - " 10.113943 10.122216 10.130334 10.138303 10.146128 10.153815\n", - " 10.161368 10.168792 10.176091 10.1832695 10.190331 10.197281\n", - " 10.20412 10.210854 10.2174835 10.224015 10.230449 10.236789\n", - " 10.243038 10.249198 10.255273 10.261263 10.267172 10.273002\n", - " 10.278753 10.2844305 10.290034 10.2955675 10.30103 ]\n" - ] - } - ], + 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[], "source": [ "# NBVAL_SKIP\n", "ssp.age.shape" @@ -655,20 +101,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(6,)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "ssp.metallicity.shape" @@ -676,20 +111,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(842,)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "ssp.wavelength.shape" @@ -697,20 +121,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(6, 221, 842)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "ssp.flux.shape" @@ -725,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -736,30 +149,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Flux [Lsun/Angstrom]')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "plt.plot(ssp.wavelength,ssp.flux[0][0])\n", @@ -770,30 +162,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Flux [Lsun/Angstrom]')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "plt.plot(ssp.wavelength,ssp.flux[-1][-1])\n", @@ -804,30 +175,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "for i in range(len(ssp.metallicity)):\n", @@ -841,30 +191,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# NBVAL_SKIP\n", "ages = np.linspace(0,len(ssp.age),10)\n", diff --git a/rubix/__init__.py b/rubix/__init__.py index 062af74a..2c3372c7 100644 --- a/rubix/__init__.py +++ b/rubix/__init__.py @@ -1,7 +1,5 @@ # The version file is generated automatically by setuptools_scm from rubix._version import version as __version__ - - from rubix.config.config import Config config = Config.load() diff --git a/rubix/config/config.py b/rubix/config/config.py index ef000249..e1c734aa 100644 --- a/rubix/config/config.py +++ b/rubix/config/config.py @@ -1,6 +1,7 @@ -from rubix.utils import read_yaml import os +from rubix.utils import read_yaml + PARENT_DIR = os.path.dirname(os.path.abspath(__file__)) RUBIX_CONFIG_PATH = os.path.join(PARENT_DIR, "rubix_config.yml") CONFIG_PATH = os.path.join( diff --git a/rubix/config/rubix_config.yml b/rubix/config/rubix_config.yml index 1767b14c..a8131661 100644 --- a/rubix/config/rubix_config.yml +++ b/rubix/config/rubix_config.yml @@ -132,7 +132,7 @@ BaseHandler: internal_energy: "erg/g" velocity: "km/s" electron_abundance: "" - temperature: "K" + temperature: "K" ssp: # units of the SSP grid that is used internally in the code diff --git a/rubix/core/cosmology.py b/rubix/core/cosmology.py index ae9c76f6..90f8089d 100644 --- a/rubix/core/cosmology.py +++ b/rubix/core/cosmology.py @@ -1,9 +1,9 @@ +from beartype import beartype as typechecker +from jaxtyping import jaxtyped + from rubix.cosmology import RubixCosmology from rubix.logger import get_logger -from jaxtyping import jaxtyped -from beartype import beartype as typechecker - @jaxtyped(typechecker=typechecker) def get_cosmology(config: dict): diff --git a/rubix/core/data.py b/rubix/core/data.py index d22b87b2..dea16588 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -432,10 +432,10 @@ def convert_to_rubix(config: Union[dict, str]): logger.info("Loading data from IllustrisAPI") api = IllustrisAPI(**config["data"]["args"], logger=logger) api.load_galaxy(**config["data"]["load_galaxy_args"]) - #else: + # else: # raise ValueError(f"Unknown data source: {config['data']['name']}.") - # Load the saved data into the input handler + # Load the saved data into the input handler logger.info("Loading data into input handler") input_handler = get_input_handler(config, logger=logger) input_handler.to_rubix(output_path=config["output_path"]) diff --git a/rubix/core/dust.py b/rubix/core/dust.py index cb6b7c76..fe15fcca 100644 --- a/rubix/core/dust.py +++ b/rubix/core/dust.py @@ -1,20 +1,22 @@ +from typing import Callable + +from beartype import beartype as typechecker +from jaxtyping import jaxtyped + +from rubix.core.cosmology import get_cosmology from rubix.logger import get_logger -from .data import RubixData from rubix.spectra.dust.dust_extinction import apply_spaxel_extinction -from .telescope import get_telescope from rubix.telescope.utils import calculate_spatial_bin_edges -from rubix.core.cosmology import get_cosmology -from typing import Callable -from jaxtyping import jaxtyped -from beartype import beartype as typechecker +from .data import RubixData +from .telescope import get_telescope @jaxtyped(typechecker=typechecker) def get_extinction(config: dict) -> Callable: """ Get the function to apply the dust extinction to the spaxel data. - + Parameters ---------- config : dict @@ -26,7 +28,7 @@ def get_extinction(config: dict) -> Callable: The function to apply the dust extinction to the spaxel data. """ logger = get_logger(config.get("logger", None)) - + # check if dust key exists in config file to ensure we really want to apply dust extinction if "dust" not in config["ssp"]: raise ValueError("Dust configuration not found in config file.") @@ -54,9 +56,10 @@ def calculate_extinction(rubixdata: RubixData) -> RubixData: """Apply the dust extinction to the spaxel data.""" logger.info("Applying dust extinction to the spaxel data...") - rubixdata.stars.spectra = apply_spaxel_extinction(config, rubixdata, wavelength, n_spaxels, spaxel_area) + rubixdata.stars.spectra = apply_spaxel_extinction( + config, rubixdata, wavelength, n_spaxels, spaxel_area + ) return rubixdata - - return calculate_extinction + return calculate_extinction diff --git a/rubix/core/fits.py b/rubix/core/fits.py index fe74655d..300bae58 100644 --- a/rubix/core/fits.py +++ b/rubix/core/fits.py @@ -1,11 +1,14 @@ +import os + +import matplotlib.pyplot as plt import numpy as np from astropy.io import fits +from matplotlib.colors import LogNorm +from mpdaf.obj import Cube + from rubix.core.telescope import get_telescope from rubix.logger import get_logger -from mpdaf.obj import Cube -import matplotlib.pyplot as plt -from matplotlib.colors import LogNorm -import os + def store_fits(config, data, filepath): """ @@ -44,7 +47,7 @@ def store_fits(config, data, filepath): hdr["ROTATION"] = config["galaxy"]["rotation"]["type"] hdr["SIM"] = config["simulation"]["name"] - #For Illustris and NIHAO + # For Illustris and NIHAO galaxy_id = config["data"]["load_galaxy_args"]["id"] snapshot = config["data"]["args"]["snapshot"] @@ -100,6 +103,7 @@ def store_fits(config, data, filepath): hdul.writeto(output_filename, overwrite=True) logger.info(f"Datacube saved to {output_filename}") + def load_fits(filepath): """ Load a FITS file and return the datacube. diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index c43772c6..18e83e0b 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -55,10 +55,10 @@ def get_calculate_spectra(config: dict) -> Callable: >>> rubixdata.stars.spectra """ logger = get_logger(config.get("logger", None)) - #lookup_interpolation_pmap = get_lookup_interpolation_pmap(config) - #lookup_interpolation_vmap = get_lookup_interpolation_vmap(config) + # lookup_interpolation_pmap = get_lookup_interpolation_pmap(config) + # lookup_interpolation_vmap = get_lookup_interpolation_vmap(config) lookup_interpolation = get_lookup_interpolation(config) - + def lookup_interpolation_laxmap(age_metallicity): age, metallicity = age_metallicity return lookup_interpolation(metallicity, age) @@ -79,16 +79,16 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: metallicity = jnp.atleast_1d(metallicity_data) # Define the chunk size (number of particles per chunk) - #chunk_size = 250000 - #total_length = metallicity.shape[ + # chunk_size = 250000 + # total_length = metallicity.shape[ # 0 - #] # assuming metallicity[0] is your 1D array of particles + # ] # assuming metallicity[0] is your 1D array of particles # List to hold the spectra chunks - #spectra_chunks = [] + # spectra_chunks = [] # Loop over the data in chunks - #for start in range(0, total_length, chunk_size): + # for start in range(0, total_length, chunk_size): # end = min(start + chunk_size, total_length) # current_chunk = lookup_interpolation( # metallicity[start:end], @@ -97,20 +97,20 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: # spectra_chunks.append(current_chunk) # Concatenate all the chunks along axis 0 - #spectra = jnp.concatenate(spectra_chunks, axis=0) + # spectra = jnp.concatenate(spectra_chunks, axis=0) # Single, batched lookup over all stars: spectra = lookup_interpolation( metallicity, age, ) - #spectra = jax.lax.map( + # spectra = jax.lax.map( # lookup_interpolation_laxmap, # (metallicity, age), # batch_size=2, - #) + # ) logger.debug(f"Calculation Finished! Spectra shape: {spectra.shape}") spectra_jax = jnp.array(spectra) - #spectra_jax = jnp.expand_dims(spectra_jax, axis=0) + # spectra_jax = jnp.expand_dims(spectra_jax, axis=0) rubixdata.stars.spectra = spectra_jax # setattr(rubixdata.gas, "spectra", spectra) # jax.debug.print("Calculate Spectra: Spectra {}", spectra) @@ -180,8 +180,8 @@ def resample_spectrum_vmap(initial_spectrum, initial_wavelength): # Parallelize the vectorized function across devices -#@jaxtyped(typechecker=typechecker) -#def get_resample_spectrum_pmap(target_wavelength) -> Callable: +# @jaxtyped(typechecker=typechecker) +# def get_resample_spectrum_pmap(target_wavelength) -> Callable: # """ # Pmap the function that resamples the spectra of the stars to the telescope wavelength grid. @@ -210,12 +210,12 @@ def get_velocities_doppler_shift_vmap( The function that doppler shifts the wavelength based on the velocity of the stars. """ - #def func(velocity): + # def func(velocity): # return velocity_doppler_shift( # wavelength=ssp_wave, velocity=velocity, direction=velocity_direction # ) - #return jax.vmap(func, in_axes=0) + # return jax.vmap(func, in_axes=0) def doppler_fn(velocities): return velocity_doppler_shift( wavelength=ssp_wave, @@ -281,14 +281,12 @@ def process_particle( logger.debug(f"Telescope Wave Seq: {telescope_wavelength.shape}") # Function to resample the spectrum to the telescope wavelength grid - #resample_spectrum_pmap = get_resample_spectrum_pmap(telescope_wavelength) - #spectrum_resampled = resample_spectrum_pmap( + # resample_spectrum_pmap = get_resample_spectrum_pmap(telescope_wavelength) + # spectrum_resampled = resample_spectrum_pmap( # particle.spectra, doppler_shifted_ssp_wave - #) + # ) resample_fn = get_resample_spectrum_vmap(telescope_wavelength) - spectrum_resampled = resample_fn( - particle.spectra, doppler_shifted_ssp_wave - ) + spectrum_resampled = resample_fn(particle.spectra, doppler_shifted_ssp_wave) return spectrum_resampled return particle.spectra @@ -328,22 +326,20 @@ def get_calculate_datacube(config: dict) -> Callable: num_spaxels = int(telescope.sbin) # Bind the num_spaxels to the function - #calculate_cube_fn = jax.tree_util.Partial(calculate_cube, num_spaxels=num_spaxels) - #calculate_cube_pmap = jax.pmap(calculate_cube_fn) + # calculate_cube_fn = jax.tree_util.Partial(calculate_cube, num_spaxels=num_spaxels) + # calculate_cube_pmap = jax.pmap(calculate_cube_fn) @jaxtyped(typechecker=typechecker) def calculate_datacube(rubixdata: RubixData) -> RubixData: logger.info("Calculating Data Cube...") - #ifu_cubes = calculate_cube_fn( + # ifu_cubes = calculate_cube_fn( # spectra=rubixdata.stars.spectra, # spaxel_index=rubixdata.stars.pixel_assignment, - #) + # ) datacube = calculate_cube( - rubixdata.stars.spectra, - rubixdata.stars.pixel_assignment, - num_spaxels + rubixdata.stars.spectra, rubixdata.stars.pixel_assignment, num_spaxels ) - #datacube = jnp.sum(ifu_cubes, axis=0) + # datacube = jnp.sum(ifu_cubes, axis=0) logger.debug(f"Datacube Shape: {datacube.shape}") # logger.debug(f"This is the datacube: {datacube}") datacube_jax = jnp.array(datacube) diff --git a/rubix/core/lsf.py b/rubix/core/lsf.py index ec72c173..7c5e9083 100644 --- a/rubix/core/lsf.py +++ b/rubix/core/lsf.py @@ -1,10 +1,13 @@ -from rubix.telescope.lsf.lsf import apply_lsf -from .telescope import get_telescope from typing import Callable + +from beartype import beartype as typechecker +from jaxtyping import jaxtyped + from rubix.logger import get_logger +from rubix.telescope.lsf.lsf import apply_lsf + from .data import RubixData -from jaxtyping import jaxtyped -from beartype import beartype as typechecker +from .telescope import get_telescope @jaxtyped(typechecker=typechecker) diff --git a/rubix/core/noise.py b/rubix/core/noise.py index 24a67f6c..a1988aa1 100644 --- a/rubix/core/noise.py +++ b/rubix/core/noise.py @@ -1,14 +1,16 @@ +from typing import Callable + import jax.numpy as jnp +from beartype import beartype as typechecker +from jaxtyping import jaxtyped + +from rubix.logger import get_logger from rubix.telescope.noise.noise import ( - calculate_noise_cube, SUPPORTED_NOISE_DISTRIBUTIONS, + calculate_noise_cube, ) + from .data import RubixData -from rubix.logger import get_logger -from .data import RubixData -from typing import Callable -from jaxtyping import jaxtyped -from beartype import beartype as typechecker @jaxtyped(typechecker=typechecker) diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index e61118a3..a2e77c58 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -1,31 +1,34 @@ +import dataclasses import time +from functools import partial from types import SimpleNamespace from typing import Union import jax import jax.numpy as jnp -from jax.tree_util import tree_flatten, tree_unflatten -from jax.tree_util import tree_map -import dataclasses # For shard_map and device mesh. import numpy as np from beartype import beartype as typechecker -from jax import block_until_ready +from jax import block_until_ready, lax +from jax.experimental.pjit import pjit from jax.experimental.shard_map import shard_map -from types import SimpleNamespace -from jax.sharding import NamedSharding -from jax.sharding import Mesh, PartitionSpec as P +from jax.sharding import Mesh, NamedSharding, PartitionSpec as P +from jax.tree_util import tree_flatten, tree_map, tree_unflatten from jaxtyping import jaxtyped -from functools import partial -from jax import lax -from jax.experimental.pjit import pjit from rubix.logger import get_logger from rubix.pipeline import linear_pipeline as pipeline from rubix.utils import get_config, get_pipeline_config -from .data import get_reshape_data, get_rubix_data +from .data import ( + Galaxy, + GasData, + RubixData, + StarsData, + get_reshape_data, + get_rubix_data, +) from .dust import get_extinction from .ifu import ( get_calculate_datacube, @@ -39,7 +42,6 @@ from .rotation import get_galaxy_rotation from .ssp import get_ssp from .telescope import get_filter_particles, get_spaxel_assignment, get_telescope -from .data import RubixData, Galaxy, StarsData, GasData class RubixPipeline: @@ -98,7 +100,7 @@ def _get_pipeline_functions(self) -> list: filter_particles = get_filter_particles(self.user_config) spaxel_assignment = get_spaxel_assignment(self.user_config) calculate_spectra = get_calculate_spectra(self.user_config) - #reshape_data = get_reshape_data(self.user_config) + # reshape_data = get_reshape_data(self.user_config) scale_spectrum_by_mass = get_scale_spectrum_by_mass(self.user_config) doppler_shift_and_resampling = get_doppler_shift_and_resampling( self.user_config @@ -114,7 +116,7 @@ def _get_pipeline_functions(self) -> list: filter_particles, spaxel_assignment, calculate_spectra, - #reshape_data, + # reshape_data, scale_spectrum_by_mass, doppler_shift_and_resampling, apply_extinction, @@ -215,67 +217,66 @@ def run_sharded(self, inputdata): num_devices = len(devices) self.logger.info("Number of devices: %d", num_devices) - mesh = Mesh(devices, axis_names = ("data",)) + mesh = Mesh(devices, axis_names=("data",)) # — sharding specs by rank — - replicate_0d = NamedSharding(mesh, P()) # for scalars - replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays - shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) - shard_1d = NamedSharding(mesh, P("data")) # for (N,) - replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube + replicate_0d = NamedSharding(mesh, P()) # for scalars + replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays + shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) + shard_1d = NamedSharding(mesh, P("data")) # for (N,) + replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube # — 1) allocate empty instances — galaxy_spec = object.__new__(Galaxy) - stars_spec = object.__new__(StarsData) - gas_spec = object.__new__(GasData) - rubix_spec = object.__new__(RubixData) + stars_spec = object.__new__(StarsData) + gas_spec = object.__new__(GasData) + rubix_spec = object.__new__(RubixData) # — 2) assign NamedSharding to each field — # galaxy - galaxy_spec.redshift = replicate_0d - galaxy_spec.center = replicate_1d + galaxy_spec.redshift = replicate_0d + galaxy_spec.center = replicate_1d galaxy_spec.halfmassrad_stars = replicate_0d # stars - stars_spec.coords = shard_2d - stars_spec.velocity = shard_2d - stars_spec.mass = shard_1d - stars_spec.age = shard_1d - stars_spec.metallicity = shard_1d - stars_spec.pixel_assignment = shard_1d - stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) - stars_spec.mask = shard_1d - stars_spec.spectra = shard_2d - stars_spec.datacube = replicate_3d + stars_spec.coords = shard_2d + stars_spec.velocity = shard_2d + stars_spec.mass = shard_1d + stars_spec.age = shard_1d + stars_spec.metallicity = shard_1d + stars_spec.pixel_assignment = shard_1d + stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + stars_spec.mask = shard_1d + stars_spec.spectra = shard_2d + stars_spec.datacube = replicate_3d # gas (same idea) - gas_spec.coords = shard_2d - gas_spec.velocity = shard_2d - gas_spec.mass = shard_1d - gas_spec.density = shard_1d - gas_spec.internal_energy = shard_1d - gas_spec.metallicity = shard_1d - gas_spec.metals = shard_1d - gas_spec.sfr = shard_1d - gas_spec.electron_abundance = shard_1d - gas_spec.pixel_assignment = shard_1d - gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) - gas_spec.mask = shard_1d - gas_spec.spectra = shard_2d - gas_spec.datacube = replicate_3d + gas_spec.coords = shard_2d + gas_spec.velocity = shard_2d + gas_spec.mass = shard_1d + gas_spec.density = shard_1d + gas_spec.internal_energy = shard_1d + gas_spec.metallicity = shard_1d + gas_spec.metals = shard_1d + gas_spec.sfr = shard_1d + gas_spec.electron_abundance = shard_1d + gas_spec.pixel_assignment = shard_1d + gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + gas_spec.mask = shard_1d + gas_spec.spectra = shard_2d + gas_spec.datacube = replicate_3d # — link them up — rubix_spec.galaxy = galaxy_spec - rubix_spec.stars = stars_spec - rubix_spec.gas = gas_spec + rubix_spec.stars = stars_spec + rubix_spec.gas = gas_spec # 1) Make a pytree of PartitionSpec partition_spec_tree = tree_map( - lambda s: s.spec if isinstance(s, NamedSharding) else None, - rubix_spec + lambda s: s.spec if isinstance(s, NamedSharding) else None, rubix_spec ) - #if the particle number is not modulo the device number, we have to padd a few empty particles + # if the particle number is not modulo the device number, we have to padd a few empty particles # to make it work # this is a bit of a hack, but it works n = inputdata.stars.coords.shape[0] @@ -283,38 +284,42 @@ def run_sharded(self, inputdata): if pad: # pad along the first axis - inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0,pad),(0,0))) - inputdata.stars.velocity = jnp.pad(inputdata.stars.velocity, ((0,pad),(0,0))) - inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0,pad))) - inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0,pad))) - inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0,pad))) + inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0, pad), (0, 0))) + inputdata.stars.velocity = jnp.pad( + inputdata.stars.velocity, ((0, pad), (0, 0)) + ) + inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0, pad))) + inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0, pad))) + inputdata.stars.metallicity = jnp.pad( + inputdata.stars.metallicity, ((0, pad)) + ) inputdata = jax.device_put(inputdata, rubix_spec) - + # create the sharded data def _shard_pipeline(sharded_rubixdata): - out_local = self.func(sharded_rubixdata) - local_cube = out_local.stars.datacube # shape (25,25,5994) + out_local = self.func(sharded_rubixdata) + local_cube = out_local.stars.datacube # shape (25,25,5994) # in‐XLA all‐reduce across the "data" axis: summed_cube = lax.psum(local_cube, axis_name="data") - return summed_cube # replicated on each device + return summed_cube # replicated on each device sharded_pipeline = shard_map( - _shard_pipeline, # the function to compile - mesh=mesh, # the mesh to use - in_specs = (partition_spec_tree,), - out_specs = replicate_3d.spec, - check_rep = False, + _shard_pipeline, # the function to compile + mesh=mesh, # the mesh to use + in_specs=(partition_spec_tree,), + out_specs=replicate_3d.spec, + check_rep=False, ) - #with mesh: + # with mesh: # inputdata = jax.device_put(inputdata, rubix_spec) - #partial_cubes = shard_pipeline(inputdata) - #full_cube = lax.psum(partial_cubes, axis_name="data") - #partial_cubes = jax.block_until_ready(partial_cubes) - #full_cube = jax.block_until_ready(full_cube) + # partial_cubes = shard_pipeline(inputdata) + # full_cube = lax.psum(partial_cubes, axis_name="data") + # partial_cubes = jax.block_until_ready(partial_cubes) + # full_cube = jax.block_until_ready(full_cube) - #full_cube = partial_cubes.sum(axis=0) + # full_cube = partial_cubes.sum(axis=0) sharded_result = sharded_pipeline(inputdata) @@ -322,10 +327,9 @@ def _shard_pipeline(sharded_rubixdata): self.logger.info( "Pipeline run completed in %.2f seconds.", time_end - time_start ) - #final_cube = jnp.sum(partial_cubes, axis=0) - - return sharded_result + # final_cube = jnp.sum(partial_cubes, axis=0) + return sharded_result def run_sharded_chunked(self, inputdata): """ @@ -365,64 +369,63 @@ def run_sharded_chunked(self, inputdata): mesh = Mesh(devices, ("data",)) # — sharding specs by rank — - replicate_0d = NamedSharding(mesh, P()) # for scalars - replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays - shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) - shard_1d = NamedSharding(mesh, P("data")) # for (N,) - replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube + replicate_0d = NamedSharding(mesh, P()) # for scalars + replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays + shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) + shard_1d = NamedSharding(mesh, P("data")) # for (N,) + replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube # — 1) allocate empty instances — galaxy_spec = object.__new__(Galaxy) - stars_spec = object.__new__(StarsData) - gas_spec = object.__new__(GasData) - rubix_spec = object.__new__(RubixData) + stars_spec = object.__new__(StarsData) + gas_spec = object.__new__(GasData) + rubix_spec = object.__new__(RubixData) # — 2) assign NamedSharding to each field — # galaxy - galaxy_spec.redshift = replicate_0d - galaxy_spec.center = replicate_1d + galaxy_spec.redshift = replicate_0d + galaxy_spec.center = replicate_1d galaxy_spec.halfmassrad_stars = replicate_0d # stars - stars_spec.coords = shard_2d - stars_spec.velocity = shard_2d - stars_spec.mass = shard_1d - stars_spec.age = shard_1d - stars_spec.metallicity = shard_1d - stars_spec.pixel_assignment = shard_1d - stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) - stars_spec.mask = shard_1d - stars_spec.spectra = shard_2d - stars_spec.datacube = replicate_3d + stars_spec.coords = shard_2d + stars_spec.velocity = shard_2d + stars_spec.mass = shard_1d + stars_spec.age = shard_1d + stars_spec.metallicity = shard_1d + stars_spec.pixel_assignment = shard_1d + stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + stars_spec.mask = shard_1d + stars_spec.spectra = shard_2d + stars_spec.datacube = replicate_3d # gas (same idea) - gas_spec.coords = shard_2d - gas_spec.velocity = shard_2d - gas_spec.mass = shard_1d - gas_spec.density = shard_1d - gas_spec.internal_energy = shard_1d - gas_spec.metallicity = shard_1d - gas_spec.metals = shard_1d - gas_spec.sfr = shard_1d - gas_spec.electron_abundance = shard_1d - gas_spec.pixel_assignment = shard_1d - gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) - gas_spec.mask = shard_1d - gas_spec.spectra = shard_2d - gas_spec.datacube = replicate_3d + gas_spec.coords = shard_2d + gas_spec.velocity = shard_2d + gas_spec.mass = shard_1d + gas_spec.density = shard_1d + gas_spec.internal_energy = shard_1d + gas_spec.metallicity = shard_1d + gas_spec.metals = shard_1d + gas_spec.sfr = shard_1d + gas_spec.electron_abundance = shard_1d + gas_spec.pixel_assignment = shard_1d + gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + gas_spec.mask = shard_1d + gas_spec.spectra = shard_2d + gas_spec.datacube = replicate_3d # — link them up — rubix_spec.galaxy = galaxy_spec - rubix_spec.stars = stars_spec - rubix_spec.gas = gas_spec + rubix_spec.stars = stars_spec + rubix_spec.gas = gas_spec # 1) Make a pytree of PartitionSpec partition_spec_tree = tree_map( - lambda s: s.spec if isinstance(s, NamedSharding) else None, - rubix_spec + lambda s: s.spec if isinstance(s, NamedSharding) else None, rubix_spec ) - #if the particle number is not modulo the device number, we have to padd a few empty particles + # if the particle number is not modulo the device number, we have to padd a few empty particles # to make it work # this is a bit of a hack, but it works telescope = get_telescope(self.user_config) @@ -440,11 +443,15 @@ def run_sharded_chunked(self, inputdata): if pad: # pad along the first axis - inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0,pad),(0,0))) - inputdata.stars.velocity = jnp.pad(inputdata.stars.velocity, ((0,pad),(0,0))) - inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0,pad))) - inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0,pad))) - inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0,pad))) + inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0, pad), (0, 0))) + inputdata.stars.velocity = jnp.pad( + inputdata.stars.velocity, ((0, pad), (0, 0)) + ) + inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0, pad))) + inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0, pad))) + inputdata.stars.metallicity = jnp.pad( + inputdata.stars.metallicity, ((0, pad)) + ) """ # Precompute all static sizes on the host @@ -466,7 +473,7 @@ def run_sharded_chunked(self, inputdata): inputdata.stars.age = jnp.pad(inputdata.stars.age, pad_width_1d) inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, pad_width_1d) """ - + # Helper to slice RubixData along axis 0 def slice_data(rubixdata, start): def slicer(x): @@ -474,29 +481,30 @@ def slicer(x): return lax.dynamic_slice_in_dim(x, start, chunk_size, axis=0) else: return x + return jax.tree_util.tree_map(slicer, rubixdata) inputdata = jax.device_put(inputdata, rubix_spec) - + # create the sharded data def _shard_pipeline(sharded_rubixdata): - out_local = self.func(sharded_rubixdata) - local_cube = out_local.stars.datacube # shape (25,25,5994) + out_local = self.func(sharded_rubixdata) + local_cube = out_local.stars.datacube # shape (25,25,5994) # in‐XLA all‐reduce across the "data" axis: summed_cube = lax.psum(local_cube, axis_name="data") - return summed_cube # replicated on each device + return summed_cube # replicated on each device sharded_pipeline = shard_map( - _shard_pipeline, # the function to compile - mesh=mesh, # the mesh to use - in_specs = (partition_spec_tree,), - out_specs = replicate_3d.spec, - check_rep = False, + _shard_pipeline, # the function to compile + mesh=mesh, # the mesh to use + in_specs=(partition_spec_tree,), + out_specs=replicate_3d.spec, + check_rep=False, ) full_cube = jnp.zeros((num_spaxels, num_spaxels, n_wave), jnp.float32) for i in range(n_chunks): # Process 4 chunks - #print(f"Processing chunk {i + 1}/{n_chunks}...") + # print(f"Processing chunk {i + 1}/{n_chunks}...") start = i * (n_stars // n_chunks) chunk_data = slice_data(inputdata, start) partial_cube = sharded_pipeline(chunk_data) @@ -505,11 +513,12 @@ def _shard_pipeline(sharded_rubixdata): full_cube = jax.block_until_ready(full_cube) time_end = time.time() - self.logger.info("Pipeline run completed in %.2f seconds.", time_end - time_start) - + self.logger.info( + "Pipeline run completed in %.2f seconds.", time_end - time_start + ) + return full_cube - - + def gradient(self): """ This function will calculate the gradient of the pipeline, but is not implemented. diff --git a/rubix/core/psf.py b/rubix/core/psf.py index 8550228a..28e8d362 100644 --- a/rubix/core/psf.py +++ b/rubix/core/psf.py @@ -1,12 +1,13 @@ -from rubix.telescope.psf.psf import get_psf_kernel, apply_psf -from rubix.logger import get_logger - -from rubix.logger import get_logger from typing import Callable, Dict + import jax.numpy as jnp -from .data import RubixData -from jaxtyping import jaxtyped from beartype import beartype as typechecker +from jaxtyping import jaxtyped + +from rubix.logger import get_logger +from rubix.telescope.psf.psf import apply_psf, get_psf_kernel + +from .data import RubixData # TODO: add option to disable PSF convolution @@ -40,7 +41,7 @@ def get_convolve_psf(config: dict) -> Callable: """ logger = get_logger(config.get("logger", None)) - + # Check if key exists in config file if "psf" not in config["telescope"]: raise ValueError("PSF configuration not found in telescope configuration") diff --git a/rubix/core/rotation.py b/rubix/core/rotation.py index c5d68b01..035931c3 100644 --- a/rubix/core/rotation.py +++ b/rubix/core/rotation.py @@ -1,8 +1,10 @@ -from rubix.logger import get_logger +from beartype import beartype as typechecker +from jaxtyping import jaxtyped + from rubix.galaxy.alignment import rotate_galaxy as rotate_galaxy_core +from rubix.logger import get_logger + from .data import RubixData -from jaxtyping import jaxtyped -from beartype import beartype as typechecker @jaxtyped(typechecker=typechecker) diff --git a/rubix/core/ssp.py b/rubix/core/ssp.py index ca5a6e65..23577a1e 100644 --- a/rubix/core/ssp.py +++ b/rubix/core/ssp.py @@ -1,12 +1,12 @@ +from typing import Callable + import jax +from beartype import beartype as typechecker +from jaxtyping import jaxtyped from rubix.logger import get_logger from rubix.spectra.ssp.factory import get_ssp_template -from typing import Callable -from jaxtyping import jaxtyped -from beartype import beartype as typechecker - @jaxtyped(typechecker=typechecker) def get_ssp(config: dict) -> object: @@ -79,7 +79,7 @@ def get_lookup_interpolation_vmap(config: dict) -> Callable: """ lookup = get_lookup_interpolation(config) lookup_vmap = jax.vmap(lookup, in_axes=(0, 0)) - + return lookup_vmap diff --git a/rubix/core/telescope.py b/rubix/core/telescope.py index 93e15a15..6cfa50be 100644 --- a/rubix/core/telescope.py +++ b/rubix/core/telescope.py @@ -1,18 +1,21 @@ +from typing import Callable, Union + import jax.numpy as jnp +from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped + +from rubix.logger import get_logger +from rubix.telescope.base import BaseTelescope +from rubix.telescope.factory import TelescopeFactory from rubix.telescope.utils import ( calculate_spatial_bin_edges, - square_spaxel_assignment, mask_particles_outside_aperture, + square_spaxel_assignment, ) -from rubix.telescope.base import BaseTelescope -from rubix.telescope.factory import TelescopeFactory -from rubix.logger import get_logger + from .cosmology import get_cosmology from .data import RubixData -from typing import Callable, Union -from jaxtyping import Array, Float, jaxtyped -from beartype import beartype as typechecker @jaxtyped(typechecker=typechecker) def get_telescope(config: Union[str, dict]) -> BaseTelescope: @@ -110,7 +113,7 @@ def spaxel_assignment(rubixdata: RubixData) -> RubixData: ) rubixdata.stars.pixel_assignment = pixel_assignment rubixdata.stars.spatial_bin_edges = spatial_bin_edges - + if rubixdata.gas.coords is not None: pixel_assignment = square_spaxel_assignment( rubixdata.gas.coords, spatial_bin_edges @@ -190,8 +193,8 @@ def filter_particles(rubixdata: RubixData) -> RubixData: mask_jax = jnp.array(mask) setattr(rubixdata.gas, "mask", mask_jax) # rubixdata.gas.mask = mask - #masked_metals = jnp.where(mask_jax[:, jnp.newaxis], rubixdata.gas.metals, 0) - #setattr(rubixdata.gas, "metals", masked_metals) + # masked_metals = jnp.where(mask_jax[:, jnp.newaxis], rubixdata.gas.metals, 0) + # setattr(rubixdata.gas, "metals", masked_metals) return rubixdata diff --git a/rubix/core/visualisation.py b/rubix/core/visualisation.py index 82f4da1c..863e90a1 100644 --- a/rubix/core/visualisation.py +++ b/rubix/core/visualisation.py @@ -1,10 +1,10 @@ -import numpy as np -import matplotlib.pyplot as plt -from mpdaf.obj import Cube +import h5py import ipywidgets as widgets +import matplotlib.pyplot as plt +import numpy as np from ipywidgets import interact from jdaviz import Cubeviz -import h5py +from mpdaf.obj import Cube def visualize_rubix(filename): diff --git a/rubix/cosmology/__init__.py b/rubix/cosmology/__init__.py index 884f3f96..11496d8b 100644 --- a/rubix/cosmology/__init__.py +++ b/rubix/cosmology/__init__.py @@ -1,4 +1,3 @@ from .base import BaseCosmology as RubixCosmology - PLANCK15 = RubixCosmology(0.3075, -1.0, 0.0, 0.6774) diff --git a/rubix/cosmology/base.py b/rubix/cosmology/base.py index eebbe15a..b5ce7d24 100644 --- a/rubix/cosmology/base.py +++ b/rubix/cosmology/base.py @@ -1,13 +1,12 @@ -from jax import lax, vmap, jit -import jax.numpy as jnp -from .utils import trapz +from typing import Union import equinox as eqx - -from typing import Union -from jaxtyping import Array, Float, jaxtyped +import jax.numpy as jnp from beartype import beartype as typechecker +from jax import jit, lax, vmap +from jaxtyping import Array, Float, jaxtyped +from .utils import trapz # TODO: maybe change this to load from the config file? C_SPEED = 2.99792458e8 # m/s diff --git a/rubix/cosmology/utils.py b/rubix/cosmology/utils.py index 70a6ac71..0579fec8 100644 --- a/rubix/cosmology/utils.py +++ b/rubix/cosmology/utils.py @@ -1,9 +1,10 @@ -from jax import jit -from jax.lax import scan from typing import Union + import jax.numpy as jnp -from jaxtyping import Array, Float, jaxtyped from beartype import beartype as typechecker +from jax import jit +from jax.lax import scan +from jaxtyping import Array, Float, jaxtyped # Source: https://github.com/ArgonneCPAC/dsps/blob/b81bac59e545e2d68ccf698faba078d87cfa2dd8/dsps/utils.py#L247C1-L256C1 diff --git a/rubix/debug.py b/rubix/debug.py index 9f4e326e..e902b82f 100644 --- a/rubix/debug.py +++ b/rubix/debug.py @@ -1,9 +1,10 @@ +import jax import jax.numpy as jnp -from rubix.galaxy.input_handler.base import create_rubix_galaxy + from rubix import config -import jax -from rubix.spectra.ssp.factory import get_ssp_template +from rubix.galaxy.input_handler.base import create_rubix_galaxy from rubix.logger import get_logger +from rubix.spectra.ssp.factory import get_ssp_template def random_data(n_particles, min_val, max_val, dimension, key=42): diff --git a/rubix/galaxy/__init__.py b/rubix/galaxy/__init__.py index 45b2e207..e5d6870f 100644 --- a/rubix/galaxy/__init__.py +++ b/rubix/galaxy/__init__.py @@ -1,6 +1,6 @@ from .input_handler import ( - IllustrisHandler, BaseHandler, IllustrisAPI, + IllustrisHandler, get_input_handler, ) diff --git a/rubix/galaxy/alignment.py b/rubix/galaxy/alignment.py index a568b57b..1398384a 100644 --- a/rubix/galaxy/alignment.py +++ b/rubix/galaxy/alignment.py @@ -1,8 +1,9 @@ -import jax.numpy as jnp from typing import Tuple, Union + +import jax.numpy as jnp +from beartype import beartype as typechecker from jax.scipy.spatial.transform import Rotation from jaxtyping import Array, Float, jaxtyped -from beartype import beartype as typechecker @jaxtyped(typechecker=typechecker) diff --git a/rubix/galaxy/input_handler/__init__.py b/rubix/galaxy/input_handler/__init__.py index 00edb73c..a7f322fd 100644 --- a/rubix/galaxy/input_handler/__init__.py +++ b/rubix/galaxy/input_handler/__init__.py @@ -1,7 +1,6 @@ -from .illustris import IllustrisHandler -from .base import BaseHandler from .api.illustris_api import IllustrisAPI +from .base import BaseHandler from .factory import get_input_handler - +from .illustris import IllustrisHandler __all__ = ["IllustrisHandler", "BaseHandler", "IllustrisAPI", "get_input_handler"] diff --git a/rubix/galaxy/input_handler/api/illustris_api.py b/rubix/galaxy/input_handler/api/illustris_api.py index 2ac26c08..4d21cb3b 100644 --- a/rubix/galaxy/input_handler/api/illustris_api.py +++ b/rubix/galaxy/input_handler/api/illustris_api.py @@ -1,7 +1,9 @@ import os -import requests -import h5py from typing import List, Union + +import h5py +import requests + from rubix import config diff --git a/rubix/galaxy/input_handler/base.py b/rubix/galaxy/input_handler/base.py index 33fe51ec..92c783a2 100644 --- a/rubix/galaxy/input_handler/base.py +++ b/rubix/galaxy/input_handler/base.py @@ -1,13 +1,15 @@ -from abc import ABC, abstractmethod -import os -import h5py import logging +import os +from abc import ABC, abstractmethod +from typing import List, Optional, Union + import astropy.units as u -from typing import List, Union, Optional +import h5py +from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped + from rubix import config from rubix.logger import get_logger -from jaxtyping import Array, Float, jaxtyped -from beartype import beartype as typechecker @jaxtyped(typechecker=typechecker) diff --git a/rubix/galaxy/input_handler/factory.py b/rubix/galaxy/input_handler/factory.py index bc888180..ce480fbc 100644 --- a/rubix/galaxy/input_handler/factory.py +++ b/rubix/galaxy/input_handler/factory.py @@ -1,10 +1,12 @@ -from .base import BaseHandler -from .illustris import IllustrisHandler -from .pynbody import PynbodyHandler from typing import Union from unittest.mock import MagicMock -from jaxtyping import Array, Float, jaxtyped + from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped + +from .base import BaseHandler +from .illustris import IllustrisHandler +from .pynbody import PynbodyHandler __all__ = ["IllustrisHandler", "BaseHandler"] diff --git a/rubix/galaxy/input_handler/illustris.py b/rubix/galaxy/input_handler/illustris.py index 8e234b5e..6051c201 100644 --- a/rubix/galaxy/input_handler/illustris.py +++ b/rubix/galaxy/input_handler/illustris.py @@ -1,9 +1,12 @@ -from .base import BaseHandler # type: ignore import os + import h5py import numpy as np -from rubix.utils import convert_values_to_physical, SFTtoAge + from rubix import config +from rubix.utils import SFTtoAge, convert_values_to_physical + +from .base import BaseHandler # type: ignore class IllustrisHandler(BaseHandler): diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index c5ce8118..d2078118 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -1,12 +1,16 @@ -from .base import BaseHandler -import pynbody -import numpy as np -from rubix.utils import SFTtoAge import logging +import os + import astropy.units as u +import numpy as np +import pynbody import yaml -import os + from rubix.units import Zsun +from rubix.utils import SFTtoAge + +from .base import BaseHandler + class PynbodyHandler(BaseHandler): def __init__( diff --git a/rubix/pipeline/abstract_pipeline.py b/rubix/pipeline/abstract_pipeline.py index 097075bb..9dec0506 100644 --- a/rubix/pipeline/abstract_pipeline.py +++ b/rubix/pipeline/abstract_pipeline.py @@ -1,7 +1,9 @@ from abc import ABC, abstractmethod -from .transformer import compiled_transformer, expression_transformer + from jax import jit +from .transformer import compiled_transformer, expression_transformer + class AbstractPipeline(ABC): """ diff --git a/rubix/pipeline/transformer.py b/rubix/pipeline/transformer.py index e23da740..0ff798a9 100644 --- a/rubix/pipeline/transformer.py +++ b/rubix/pipeline/transformer.py @@ -1,7 +1,6 @@ from copy import deepcopy -from jax import jit -from jax import make_jaxpr +from jax import jit, make_jaxpr from jax.tree_util import Partial diff --git a/rubix/spectra/dust/dust_baseclasses.py b/rubix/spectra/dust/dust_baseclasses.py index eea4224a..e6fe89fb 100644 --- a/rubix/spectra/dust/dust_baseclasses.py +++ b/rubix/spectra/dust/dust_baseclasses.py @@ -1,18 +1,22 @@ -import jax.numpy as jnp -import equinox from abc import abstractmethod -#TODO: add runtime type checking for valid x ranges +import equinox +import jax.numpy as jnp +from beartype import beartype as typechecker + +# TODO: add runtime type checking for valid x ranges # can be achieved by using chekify... -#from .helpers import test_valid_x_range +# from .helpers import test_valid_x_range from jaxtyping import Array, Float, jaxtyped -from beartype import beartype as typechecker -__all__ = ["BaseExtModel", "BaseExtRvModel"]#, "BaseExtRvAfAModel", "BaseExtGrainModel"] - +__all__ = [ + "BaseExtModel", + "BaseExtRvModel", +] # , "BaseExtRvAfAModel", "BaseExtGrainModel"] + @jaxtyped(typechecker=typechecker) -class BaseExtModel(equinox.Module): +class BaseExtModel(equinox.Module): """ Base class for dust extinction models. """ @@ -25,8 +29,8 @@ def __call__(self, wave: Float[Array, "n_wave"]) -> Float[Array, "n_wave"]: Evaluate the dust extinction model at the input wavelength for the given model parameters. """ - #test_valid_x_range(wave, [self.wave_range_l,self.wave_range_h], self.__class__.__name__) - + # test_valid_x_range(wave, [self.wave_range_l,self.wave_range_h], self.__class__.__name__) + return self.evaluate(wave) @abstractmethod @@ -71,15 +75,14 @@ class BaseExtRvModel(BaseExtModel): """ Rv: equinox.AbstractVar[float] - Rv_range_l: equinox.AbstractVar[float]#[Array, "2"]] + Rv_range_l: equinox.AbstractVar[float] # [Array, "2"]] Rv_range_h: equinox.AbstractVar[float] """ - The Rv parameter (R(V) = A(V)/E(B-V) total-to-selective extinction) of the dust extinction model and its valid range. + The Rv parameter (R(V) = A(V)/E(B-V) total-to-selective extinction) of the dust extinction model and its valid range. """ - - #def __check_init__(self) -> None: + # def __check_init__(self) -> None: # """ # Check if the Rv parameter of the dust extinction model is within Rv_range. @@ -87,7 +90,7 @@ class BaseExtRvModel(BaseExtModel): # ---------- # Rv : Float # The Rv parameter of the dust extinction model. - + # Raises # ------ # ValueError @@ -112,15 +115,16 @@ class BaseExtRvModel(BaseExtModel): # condition = jnp.logical_or(self.Rv < self.Rv_range_l, self.Rv > self.Rv_range_h) # jax.debug.print("Condition: {}", condition) - # jax.lax.cond( # jnp.logical_or(self.Rv < self.Rv_range_l, self.Rv > self.Rv_range_h), # true_fn, # false_fn, # operand=None # ) - - def extinguish(self, wave: Float[Array, "n_wave"], Av: Float = None, Ebv: Float = None) -> Float[Array, "n_wave"]: + + def extinguish( + self, wave: Float[Array, "n_wave"], Av: Float = None, Ebv: Float = None + ) -> Float[Array, "n_wave"]: """ Calculate the dust extinction for a given wavelength as a fraction. @@ -134,7 +138,7 @@ def extinguish(self, wave: Float[Array, "n_wave"], Av: Float = None, Ebv: Float The visual extinction. A(V) value of dust column. Note: Av or Ebv must be set. - + Ebv : Float The color excess. E(B-V) value of dust column. diff --git a/rubix/spectra/dust/extinction_models.py b/rubix/spectra/dust/extinction_models.py index e3f83e93..5bae1dc1 100644 --- a/rubix/spectra/dust/extinction_models.py +++ b/rubix/spectra/dust/extinction_models.py @@ -1,15 +1,16 @@ -import jax.numpy as jnp import equinox +import jax.numpy as jnp +from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped from .dust_baseclasses import BaseExtRvModel +from .generic_models import FM90, Drude1d, Polynomial1d, PowerLaw1d, _modified_drude from .helpers import _smoothstep -from .generic_models import PowerLaw1d, Polynomial1d, Drude1d, _modified_drude, FM90 - -from jaxtyping import Array, Float, jaxtyped -from beartype import beartype as typechecker - -RV_MODELS = ["Cardelli89", "Gordon23"] #"O94", "F99", "F04", "VCG04", "GCC09", "M14", "G16", "F19", "D22", "G23"] +RV_MODELS = [ + "Cardelli89", + "Gordon23", +] # "O94", "F99", "F04", "VCG04", "GCC09", "M14", "G16", "F19", "D22", "G23"] wave_range_CCM89 = [0.3, 10.0] Rv_range_CCM89 = [2.0, 6.0] @@ -17,11 +18,12 @@ wave_range_G23 = [0.0912, 32.0] Rv_range_G23 = [2.3, 5.6] + @equinox.filter_jit @jaxtyped(typechecker=typechecker) class Cardelli89(BaseExtRvModel): r""" - Calculate the extinction curve of the Milky Way according to the + Calculate the extinction curve of the Milky Way according to the Cardelli, Clayton, & Mathis (1989) Milky Way R(V) dependent model. Parameters @@ -80,30 +82,38 @@ class Cardelli89(BaseExtRvModel): plt.show() """ - #wave: Float[Array, "n_wave"] - - #wave_range: Float[Array, "2"] = equinox.field(converter=jnp.asarray, static=True, default_factory=lambda: jnp.array(wave_range_CCM89)) - wave_range_l: float = equinox.field(converter=float, static=True, default=wave_range_CCM89[0]) - wave_range_h: float = equinox.field(converter=float, static=True, default=wave_range_CCM89[1]) - + # wave: Float[Array, "n_wave"] + + # wave_range: Float[Array, "2"] = equinox.field(converter=jnp.asarray, static=True, default_factory=lambda: jnp.array(wave_range_CCM89)) + wave_range_l: float = equinox.field( + converter=float, static=True, default=wave_range_CCM89[0] + ) + wave_range_h: float = equinox.field( + converter=float, static=True, default=wave_range_CCM89[1] + ) + Rv: float = equinox.field(converter=float, static=True, default=3.1) - #Rv_range: Float[Array, "2"] = equinox.field(converter=jnp.asarray, static=True, default_factory=lambda: jnp.array(Rv_range_CCM89)) - Rv_range_l: float = equinox.field(converter=float, static=True, default=Rv_range_CCM89[0]) - Rv_range_h: float = equinox.field(converter=float, static=True, default=Rv_range_CCM89[1]) + # Rv_range: Float[Array, "2"] = equinox.field(converter=jnp.asarray, static=True, default_factory=lambda: jnp.array(Rv_range_CCM89)) + Rv_range_l: float = equinox.field( + converter=float, static=True, default=Rv_range_CCM89[0] + ) + Rv_range_h: float = equinox.field( + converter=float, static=True, default=Rv_range_CCM89[1] + ) def evaluate(self, wave: Float[Array, "n_wave"]) -> Float[Array, "n_wave"]: """ - Cardelli, Clayton, and Mathis (1989, ApJ, 345, 245) function + Cardelli, Clayton, and Mathis (1989, ApJ, 345, 245) function - Parameters - ---------- - wave: float - expects wave as wavelengths in microns. + Parameters + ---------- + wave: float + expects wave as wavelengths in microns. - Returns - ------- - axav: jax numpy array (float) - A(wave)/A(V) extinction curve [mag] + Returns + ------- + axav: jax numpy array (float) + A(wave)/A(V) extinction curve [mag] """ # setup the a & b coefficient vectors @@ -118,23 +128,40 @@ def evaluate(self, wave: Float[Array, "n_wave"]) -> Float[Array, "n_wave"]: fuv_mask = jnp.logical_and(8 < wave, wave <= 10) # Infrared - a = jnp.where(ir_mask, 0.574 * wave ** 1.61, a) - b = jnp.where(ir_mask, -0.527 * wave ** 1.61, b) + a = jnp.where(ir_mask, 0.574 * wave**1.61, a) + b = jnp.where(ir_mask, -0.527 * wave**1.61, b) # NIR/optical y = wave - 1.82 - a = jnp.where(opt_mask, 1 + 0.17699*y - 0.50447*y**2 - 0.02427*y**3 + 0.72085*y**4 + 0.01979*y**5 - 0.77530*y**6 + 0.32999*y**7, a) - b = jnp.where(opt_mask, 1.41338*y + 2.28305*y**2 + 1.07233*y**3 - 5.38434*y**4 - 0.62251*y**5 + 5.30260*y**6 - 2.09002*y**7, b) + a = jnp.where( + opt_mask, + 1 + + 0.17699 * y + - 0.50447 * y**2 + - 0.02427 * y**3 + + 0.72085 * y**4 + + 0.01979 * y**5 + - 0.77530 * y**6 + + 0.32999 * y**7, + a, + ) + b = jnp.where( + opt_mask, + 1.41338 * y + + 2.28305 * y**2 + + 1.07233 * y**3 + - 5.38434 * y**4 + - 0.62251 * y**5 + + 5.30260 * y**6 + - 2.09002 * y**7, + b, + ) a = jnp.where( - nuv_mask, - 1.752 - 0.316 * wave - 0.104 / ((wave - 4.67) ** 2 + 0.341), - a + nuv_mask, 1.752 - 0.316 * wave - 0.104 / ((wave - 4.67) ** 2 + 0.341), a ) b = jnp.where( - nuv_mask, - -3.09 + 1.825 * wave + 1.206 / ((wave - 4.62) ** 2 + 0.263), - b + nuv_mask, -3.09 + 1.825 * wave + 1.206 / ((wave - 4.62) ** 2 + 0.263), b ) # far-NUV @@ -144,8 +171,8 @@ def evaluate(self, wave: Float[Array, "n_wave"]) -> Float[Array, "n_wave"]: # FUV y = wave - 8.0 - a = jnp.where(fuv_mask, -1.073 - 0.628*y + 0.137*y**2 - 0.070*y**3, a) - b = jnp.where(fuv_mask, 13.670 + 4.257*y - 0.420*y**2 + 0.374*y**3, b) + a = jnp.where(fuv_mask, -1.073 - 0.628 * y + 0.137 * y**2 - 0.070 * y**3, a) + b = jnp.where(fuv_mask, 13.670 + 4.257 * y - 0.420 * y**2 + 0.374 * y**3, b) # return A(x)/A(V) return a + b / self.Rv @@ -205,18 +232,25 @@ class Gordon23(BaseExtRvModel): ax.legend(loc='best') plt.show() """ - - #wave_range: ClassVar[Float[Array, "2"]] = equinox.field(converter=jnp.asarray, static=True, default=jnp.array(wave_range_G23)) - #Rv_range: ClassVar[Float[Array, "2"]] = equinox.field(converter=jnp.asarray, static=True, default=jnp.array(Rv_range_G23)) - wave_range_l: float = equinox.field(converter=float, static=True, default=wave_range_G23[0]) - wave_range_h: float = equinox.field(converter=float, static=True, default=wave_range_G23[1]) - - Rv: float = equinox.field(converter=float, static=True, default=3.1) - #Rv_range: Float[Array, "2"] = equinox.field(converter=jnp.asarray, static=True, default_factory=lambda: jnp.array(Rv_range_CCM89)) - Rv_range_l: float = equinox.field(converter=float, static=True, default=Rv_range_G23[0]) - Rv_range_h: float = equinox.field(converter=float, static=True, default=Rv_range_G23[1]) + # wave_range: ClassVar[Float[Array, "2"]] = equinox.field(converter=jnp.asarray, static=True, default=jnp.array(wave_range_G23)) + # Rv_range: ClassVar[Float[Array, "2"]] = equinox.field(converter=jnp.asarray, static=True, default=jnp.array(Rv_range_G23)) + + wave_range_l: float = equinox.field( + converter=float, static=True, default=wave_range_G23[0] + ) + wave_range_h: float = equinox.field( + converter=float, static=True, default=wave_range_G23[1] + ) + Rv: float = equinox.field(converter=float, static=True, default=3.1) + # Rv_range: Float[Array, "2"] = equinox.field(converter=jnp.asarray, static=True, default_factory=lambda: jnp.array(Rv_range_CCM89)) + Rv_range_l: float = equinox.field( + converter=float, static=True, default=Rv_range_G23[0] + ) + Rv_range_h: float = equinox.field( + converter=float, static=True, default=Rv_range_G23[1] + ) def evaluate(self, wave: Float[Array, "n_wave"]) -> Float[Array, "n_wave"]: """ @@ -252,7 +286,6 @@ def evaluate(self, wave: Float[Array, "n_wave"]) -> Float[Array, "n_wave"]: uvopt_waves = [0.3, 0.33] uvopt_overlap = jnp.logical_and(wave >= uvopt_waves[0], wave <= uvopt_waves[1]) - # NIR/MIR # fmt: off # (scale, alpha1, alpha2, swave, swidth), sil1, sil2 @@ -262,7 +295,9 @@ def evaluate(self, wave: Float[Array, "n_wave"]) -> Float[Array, "n_wave"]: # fmt: on a = jnp.where(ir_mask, self.nirmir_intercept(wave, ir_a), a) - b = jnp.where(ir_mask, PowerLaw1d(x=wave, amplitude=-1.01251, x_0=1.0, alpha=-1.06099), b) + b = jnp.where( + ir_mask, PowerLaw1d(x=wave, amplitude=-1.01251, x_0=1.0, alpha=-1.06099), b + ) # optical # fmt: off @@ -277,7 +312,9 @@ def evaluate(self, wave: Float[Array, "n_wave"]) -> Float[Array, "n_wave"]: 0.1713 , 1.587, 0.243] # fmt: on - def compound_polynomial_drude_model(x: Float[Array, "n_wave"], params: Float[Array, "m"]) -> Float[Array, "n_wave"]: + def compound_polynomial_drude_model( + x: Float[Array, "n_wave"], params: Float[Array, "m"] + ) -> Float[Array, "n_wave"]: """ Compound polynomial and Drude model @@ -301,36 +338,65 @@ def compound_polynomial_drude_model(x: Float[Array, "n_wave"], params: Float[Arr poly_result = Polynomial1d(x, poly_coeffs) # Evaluate the Drude models - drude_result_1 = Drude1d(x, amplitude=drude_params[0], x_0=drude_params[1], fwhm=drude_params[2]) - drude_result_2 = Drude1d(x, amplitude=drude_params[3], x_0=drude_params[4], fwhm=drude_params[5]) - drude_result_3 = Drude1d(x, amplitude=drude_params[6], x_0=drude_params[7], fwhm=drude_params[8]) + drude_result_1 = Drude1d( + x, amplitude=drude_params[0], x_0=drude_params[1], fwhm=drude_params[2] + ) + drude_result_2 = Drude1d( + x, amplitude=drude_params[3], x_0=drude_params[4], fwhm=drude_params[5] + ) + drude_result_3 = Drude1d( + x, amplitude=drude_params[6], x_0=drude_params[7], fwhm=drude_params[8] + ) # Combine the results return poly_result + drude_result_1 + drude_result_2 + drude_result_3 - + a = jnp.where(opt_mask, compound_polynomial_drude_model(1 / wave, opt_a), a) b = jnp.where(opt_mask, compound_polynomial_drude_model(1 / wave, opt_b), b) - # overlap between optical/ir weights = _smoothstep(wave, x_min=optir_waves[0], x_max=optir_waves[1], N=1) - a = jnp.where(optir_overlap, (1.0 - weights) * compound_polynomial_drude_model(1 / wave, opt_a) + weights * self.nirmir_intercept(wave, ir_a), a) - b = jnp.where(optir_overlap, (1.0 - weights) * compound_polynomial_drude_model(1 / wave, opt_b) + weights * PowerLaw1d(x=wave, amplitude=-1.01251, x_0=1.0, alpha=-1.06099), b) + a = jnp.where( + optir_overlap, + (1.0 - weights) * compound_polynomial_drude_model(1 / wave, opt_a) + + weights * self.nirmir_intercept(wave, ir_a), + a, + ) + b = jnp.where( + optir_overlap, + (1.0 - weights) * compound_polynomial_drude_model(1 / wave, opt_b) + + weights * PowerLaw1d(x=wave, amplitude=-1.01251, x_0=1.0, alpha=-1.06099), + b, + ) # Ultraviolet - a = jnp.where(uv_mask, FM90(1 / wave, 0.81297, 0.2775, 1.06295, 0.11303, 4.60, 0.99), a) - b = jnp.where(uv_mask, FM90(1 / wave, -2.97868, 1.89808, 3.10334, 0.65484, 4.60, 0.99), b) + a = jnp.where( + uv_mask, FM90(1 / wave, 0.81297, 0.2775, 1.06295, 0.11303, 4.60, 0.99), a + ) + b = jnp.where( + uv_mask, FM90(1 / wave, -2.97868, 1.89808, 3.10334, 0.65484, 4.60, 0.99), b + ) # overlap between uv/optical weights = _smoothstep(wave, x_min=uvopt_waves[0], x_max=uvopt_waves[1], N=1) - a = jnp.where(uvopt_overlap, (1.0 - weights) * FM90(1 / wave, 0.81297, 0.2775, 1.06295, 0.11303, 4.60, 0.99) + weights * compound_polynomial_drude_model(1 / wave, opt_a), a) - b = jnp.where(uvopt_overlap, (1.0 - weights) * FM90(1 / wave, -2.97868, 1.89808, 3.10334, 0.65484, 4.60, 0.99) + weights * compound_polynomial_drude_model(1 / wave, opt_b), b) + a = jnp.where( + uvopt_overlap, + (1.0 - weights) + * FM90(1 / wave, 0.81297, 0.2775, 1.06295, 0.11303, 4.60, 0.99) + + weights * compound_polynomial_drude_model(1 / wave, opt_a), + a, + ) + b = jnp.where( + uvopt_overlap, + (1.0 - weights) + * FM90(1 / wave, -2.97868, 1.89808, 3.10334, 0.65484, 4.60, 0.99) + + weights * compound_polynomial_drude_model(1 / wave, opt_b), + b, + ) # return A(x)/A(V) return a + b * (1 / self.Rv - 1 / 3.1) - - @staticmethod def nirmir_intercept(wave, params): """ @@ -376,10 +442,10 @@ def nirmir_intercept(wave, params): return axav -#TODO: Implement more jax versions of extinction models from astropy, see https://dust-extinction.readthedocs.io/en/latest/index.html +# TODO: Implement more jax versions of extinction models from astropy, see https://dust-extinction.readthedocs.io/en/latest/index.html # Create a dictionary to map model names to classes Rv_model_dict = { "Cardelli89": Cardelli89, "Gordon23": Gordon23, -} \ No newline at end of file +} diff --git a/rubix/spectra/dust/generic_models.py b/rubix/spectra/dust/generic_models.py index be60c0e0..025dd9b9 100644 --- a/rubix/spectra/dust/generic_models.py +++ b/rubix/spectra/dust/generic_models.py @@ -1,18 +1,23 @@ +from typing import Tuple + import jax.numpy as jnp +from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped from .helpers import poly_map_domain -#TODO: add runtime type checking for valid x ranges + +# TODO: add runtime type checking for valid x ranges # can be achieved by using chekify... -#from .dust_baseclasses import test_valid_x_range +# from .dust_baseclasses import test_valid_x_range -from typing import Tuple -from jaxtyping import Array, Float, jaxtyped -from beartype import beartype as typechecker -#TODO: Implement functions as classes? +# TODO: Implement functions as classes? + @jaxtyped(typechecker=typechecker) -def PowerLaw1d(x: Float[Array, "n_wave"], amplitude: float, x_0: float, alpha: float) -> Float[Array, "n_wave"]: +def PowerLaw1d( + x: Float[Array, "n_wave"], amplitude: float, x_0: float, alpha: float +) -> Float[Array, "n_wave"]: """ Calculate a power law function. Function inspired by astropy.modeling.functional_models.PowerLaw1D. @@ -32,7 +37,7 @@ def PowerLaw1d(x: Float[Array, "n_wave"], amplitude: float, x_0: float, alpha: f ------- Float[Array, "n_wave"] Output array after applying the power law. - + Notes ----- Model formula (with :math:`A` for ``amplitude`` and :math:`\\alpha` for ``alpha``): @@ -43,15 +48,20 @@ def PowerLaw1d(x: Float[Array, "n_wave"], amplitude: float, x_0: float, alpha: f return amplitude * xx ** (-alpha) -def Polynomial1d(x: Float[Array, "n"], coeffs: Float[Array, "m"], domain: Tuple[float, float] = (-1., 1.), window: Tuple[float, float] = (-1., 1.)) -> Float[Array, "n"]: +def Polynomial1d( + x: Float[Array, "n"], + coeffs: Float[Array, "m"], + domain: Tuple[float, float] = (-1.0, 1.0), + window: Tuple[float, float] = (-1.0, 1.0), +) -> Float[Array, "n"]: r""" - Evaluate a 1D polynomial model defined as + Evaluate a 1D polynomial model defined as .. math:: P = \sum_{i=0}^{i=n}C_{i} * x^{i} - - This function inspired by astropy.modelling.polynomial.Polynomial1D. + + This function inspired by astropy.modelling.polynomial.Polynomial1D. Parameters ---------- @@ -85,8 +95,11 @@ def horner(x: Float[Array, "n"], coeffs: Float[Array, "m"]) -> Float[Array, "n"] x = poly_map_domain(x, domain, window) return horner(x, coeffs) + @jaxtyped(typechecker=typechecker) -def Drude1d(x: Float[Array, "n"], amplitude: float = 1.0, x_0: float = 1.0, fwhm: float = 1.0): +def Drude1d( + x: Float[Array, "n"], amplitude: float = 1.0, x_0: float = 1.0, fwhm: float = 1.0 +): r""" Evaluate the Drude model function. This function is inspired by astropy.modeling.functional_models.Drude1D. @@ -110,7 +123,7 @@ def Drude1d(x: Float[Array, "n"], amplitude: float = 1.0, x_0: float = 1.0, fwhm ------- result : ndarray Evaluated Drude model values. - + Examples -------- .. plot:: @@ -137,13 +150,14 @@ def Drude1d(x: Float[Array, "n"], amplitude: float = 1.0, x_0: float = 1.0, fwhm if x_0 == 0: raise ValueError("0 is not an allowed value for x_0") return ( - amplitude - * ((fwhm / x_0) ** 2) - / ((x / x_0 - x_0 / x) ** 2 + (fwhm / x_0) ** 2) + amplitude * ((fwhm / x_0) ** 2) / ((x / x_0 - x_0 / x) ** 2 + (fwhm / x_0) ** 2) ) + @jaxtyped(typechecker=typechecker) -def _modified_drude(x: Float[Array, "n"], scale: float, x_o: float, gamma_o: float, asym: float) -> Float[Array, "n"]: +def _modified_drude( + x: Float[Array, "n"], scale: float, x_o: float, gamma_o: float, asym: float +) -> Float[Array, "n"]: """ Modified Drude function to have a variable asymmetry. Drude profiles are intrinsically asymmetric with the asymmetry fixed by specific central @@ -167,7 +181,7 @@ def _modified_drude(x: Float[Array, "n"], scale: float, x_o: float, gamma_o: flo asym : float asymmetry where a value of 0 results in a standard Drude profile - + Returns ------- y : ndarray @@ -178,8 +192,17 @@ def _modified_drude(x: Float[Array, "n"], scale: float, x_o: float, gamma_o: flo return y + @jaxtyped(typechecker=typechecker) -def FM90(x: Float[Array, "n"], C1: float = 0.10, C2: float = 0.70, C3: float = 3.23, C4: float = 0.41, xo: float = 4.59, gamma: float = 0.95) -> Float[Array, "n"]: +def FM90( + x: Float[Array, "n"], + C1: float = 0.10, + C2: float = 0.70, + C3: float = 3.23, + C4: float = 0.41, + xo: float = 4.59, + gamma: float = 0.95, +) -> Float[Array, "n"]: r""" Fitzpatrick & Massa (1990) 6 parameter ultraviolet shape model @@ -274,9 +297,9 @@ def FM90(x: Float[Array, "n"], C1: float = 0.10, C2: float = 0.70, C3: float = 3 "C3": (-1.0, 6.0), "C4": (-0.5, 1.5), "xo": (4.5, 4.9), - "gamma": (0.6, 1.7) + "gamma": (0.6, 1.7), } - + # Check if parameters are within bounds if not (bounds["C1"][0] <= C1 <= bounds["C1"][1]): raise ValueError(f"C1 is out of bounds: {C1}") @@ -292,8 +315,7 @@ def FM90(x: Float[Array, "n"], C1: float = 0.10, C2: float = 0.70, C3: float = 3 raise ValueError(f"gamma is out of bounds: {gamma}") x_range = [1 / 0.35, 1 / 0.09] - #test_valid_x_range(x, x_range, "FM90") - + # test_valid_x_range(x, x_range, "FM90") # linear term exvebv = C1 + C2 * x @@ -305,7 +327,9 @@ def FM90(x: Float[Array, "n"], C1: float = 0.10, C2: float = 0.70, C3: float = 3 # FUV rise term fnuv_mask = x >= 5.9 y = jnp.where(fnuv_mask, x - 5.9, 0.0) - exvebv = jnp.where(fnuv_mask, exvebv + C4 * (0.5392 * (y**2) + 0.05644 * (y**3)), exvebv) + exvebv = jnp.where( + fnuv_mask, exvebv + C4 * (0.5392 * (y**2) + 0.05644 * (y**3)), exvebv + ) # return E(x-V)/E(B-V) - return exvebv \ No newline at end of file + return exvebv diff --git a/rubix/spectra/dust/helpers.py b/rubix/spectra/dust/helpers.py index a3837e4c..1c174b35 100644 --- a/rubix/spectra/dust/helpers.py +++ b/rubix/spectra/dust/helpers.py @@ -1,14 +1,22 @@ -import jax.numpy as jnp -import jax -#from jax.scipy.special import comb -from scipy.special import comb #whenever there is a jax version of comb, replace this!!! -#Might come soon according to this github PR: https://github.com/jax-ml/jax/pull/18389 - from typing import Tuple -from jaxtyping import Array, Float, jaxtyped + +import jax +import jax.numpy as jnp from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped + +# from jax.scipy.special import comb +from scipy.special import ( # whenever there is a jax version of comb, replace this!!! + comb, +) -def test_valid_x_range(wave: Float[Array, "n"], wave_range: Float[Array, "2"], outname: str) -> None: # pragma no cover +# Might come soon according to this github PR: https://github.com/jax-ml/jax/pull/18389 + + + +def test_valid_x_range( + wave: Float[Array, "n"], wave_range: Float[Array, "2"], outname: str +) -> None: # pragma no cover """ Test if the input wavelength is within the valid range of the model. @@ -16,10 +24,10 @@ def test_valid_x_range(wave: Float[Array, "n"], wave_range: Float[Array, "2"], o ---------- wave : Float[Array, "n"] The input wavelength to test. - + wave_range : Float[Array, "2"] The valid range of the model. - + outname : str The name of the model for error message. @@ -29,9 +37,10 @@ def test_valid_x_range(wave: Float[Array, "n"], wave_range: Float[Array, "2"], o """ deltacheck = 1e-6 # delta to allow for small numerical issues - #if jnp.logical_or( + + # if jnp.logical_or( # jnp.any(wave <= (wave_range[0] - deltacheck)), jnp.any(wave >= (wave_range[1] + deltacheck)) - #): + # ): # raise ValueError( # "Input wave outside of range defined for " # + outname @@ -50,12 +59,16 @@ def false_fn(_): return None condition = jnp.logical_or( - jnp.any(wave <= (wave_range[0] - deltacheck)), jnp.any(wave >= (wave_range[1] + deltacheck)) + jnp.any(wave <= (wave_range[0] - deltacheck)), + jnp.any(wave >= (wave_range[1] + deltacheck)), ) jax.lax.cond(condition, true_fn, false_fn, operand=None) - + + @jaxtyped(typechecker=typechecker) -def _smoothstep(x: Float[Array, "n_wave"], x_min: float = 0, x_max: float = 1, N: int = 1) -> Float[Array, "n_wave"]: +def _smoothstep( + x: Float[Array, "n_wave"], x_min: float = 0, x_max: float = 1, N: int = 1 +) -> Float[Array, "n_wave"]: """ Smoothstep function. This function is a polynomial approximation to the smoothstep function. The smoothstep function is a function commonly used in computer graphics to interpolate smoothly between two values. @@ -70,8 +83,11 @@ def _smoothstep(x: Float[Array, "n_wave"], x_min: float = 0, x_max: float = 1, N return result + @jaxtyped(typechecker=typechecker) -def poly_map_domain(oldx: Float[Array, "n"], domain: Tuple[float, float], window: Tuple[float, float]) -> Float[Array, "n"]: +def poly_map_domain( + oldx: Float[Array, "n"], domain: Tuple[float, float], window: Tuple[float, float] +) -> Float[Array, "n"]: """ Map domain into window by shifting and scaling. @@ -89,4 +105,4 @@ def poly_map_domain(oldx: Float[Array, "n"], domain: Tuple[float, float], window scl = (window[1] - window[0]) / (domain[1] - domain[0]) off = (window[0] * domain[1] - window[1] * domain[0]) / (domain[1] - domain[0]) - return off + scl * oldx \ No newline at end of file + return off + scl * oldx diff --git a/rubix/spectra/ssp/factory.py b/rubix/spectra/ssp/factory.py index 916d9fab..69f2e7c6 100644 --- a/rubix/spectra/ssp/factory.py +++ b/rubix/spectra/ssp/factory.py @@ -1,12 +1,12 @@ -from rubix.utils import read_yaml -from rubix.spectra.ssp.grid import SSPGrid, HDF5SSPGrid, pyPipe3DSSPGrid -from rubix.spectra.ssp.fsps_grid import write_fsps_data_to_disk +from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped + from rubix import config as rubix_config -from rubix.paths import TEMPLATE_PATH from rubix.logger import get_logger - -from jaxtyping import Array, Float, jaxtyped -from beartype import beartype as typechecker +from rubix.paths import TEMPLATE_PATH +from rubix.spectra.ssp.fsps_grid import write_fsps_data_to_disk +from rubix.spectra.ssp.grid import HDF5SSPGrid, SSPGrid, pyPipe3DSSPGrid +from rubix.utils import read_yaml @jaxtyped(typechecker=typechecker) @@ -45,7 +45,9 @@ def get_ssp_template(template: str) -> SSPGrid: elif config[template]["format"].lower() == "fsps": if config[template]["source"] == "load_from_file": try: - return HDF5SSPGrid.from_file(config[template], file_location=TEMPLATE_PATH) + return HDF5SSPGrid.from_file( + config[template], file_location=TEMPLATE_PATH + ) except FileNotFoundError: logger.warning( "The FSPS SSP template file is not found. Running FSPS to generate SSP templates." @@ -53,7 +55,9 @@ def get_ssp_template(template: str) -> SSPGrid: write_fsps_data_to_disk( config[template]["file_name"], file_location=TEMPLATE_PATH ) - return HDF5SSPGrid.from_file(config[template], file_location=TEMPLATE_PATH) + return HDF5SSPGrid.from_file( + config[template], file_location=TEMPLATE_PATH + ) elif config[template]["source"] == "rerun_from_scratch": logger.info( "Running fsps to generate SSP templates. This may take a while." @@ -69,4 +73,4 @@ def get_ssp_template(template: str) -> SSPGrid: else: raise ValueError( "Currently only HDF5 format and fits files in the format of pyPipe3D format are supported for SSP templates." - ) \ No newline at end of file + ) diff --git a/rubix/spectra/ssp/fsps_grid.py b/rubix/spectra/ssp/fsps_grid.py index 555343c7..e76e10f6 100644 --- a/rubix/spectra/ssp/fsps_grid.py +++ b/rubix/spectra/ssp/fsps_grid.py @@ -1,16 +1,19 @@ """Use python-fsps to retrieve a block of Simple Stellar Population (SSP) data adapted from https://github.com/ArgonneCPAC/dsps/blob/main/dsps/data_loaders/retrieve_fsps_data.py""" +import importlib +import os + +import h5py import numpy as np -from rubix.logger import get_logger +from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped + from rubix import config as rubix_config +from rubix.logger import get_logger from rubix.paths import TEMPLATE_PATH -import h5py -import os -import importlib + from .grid import SSPGrid -from jaxtyping import Array, Float, jaxtyped -from beartype import beartype as typechecker # Setup a logger based on the config logger = get_logger() diff --git a/rubix/spectra/ssp/grid.py b/rubix/spectra/ssp/grid.py index 98259e21..6800c23f 100644 --- a/rubix/spectra/ssp/grid.py +++ b/rubix/spectra/ssp/grid.py @@ -1,18 +1,20 @@ +import os +from dataclasses import dataclass, fields +from typing import List, Tuple, Union + import equinox as eqx +import h5py import jax.numpy as jnp +import requests from astropy import units as u from astropy.io import fits -import os -import h5py -import requests -from rubix import config as rubix_config -from rubix.logger import get_logger +from beartype import beartype as typechecker from interpax import interp2d from jax.tree_util import Partial -from dataclasses import dataclass, fields -from typing import List, Tuple, Union -from jaxtyping import Int, Array, Float, jaxtyped -from beartype import beartype as typechecker +from jaxtyping import Array, Float, Int, jaxtyped + +from rubix import config as rubix_config +from rubix.logger import get_logger SSP_UNITS = rubix_config["ssp"]["units"] diff --git a/rubix/telescope/apertures.py b/rubix/telescope/apertures.py index b9021ef5..2b01cf99 100644 --- a/rubix/telescope/apertures.py +++ b/rubix/telescope/apertures.py @@ -2,11 +2,10 @@ """ -import numpy as np -from jaxtyping import Array, Float import jax.numpy as jnp -from jaxtyping import Array, Float, jaxtyped +import numpy as np from beartype import beartype as typechecker +from jaxtyping import Array, Float, jaxtyped __all__ = ["HEXAGONAL_APERTURE", "SQUARE_APERTURE", "CIRCULAR_APERTURE"] diff --git a/rubix/telescope/base.py b/rubix/telescope/base.py index f2761f4c..91bda2a5 100644 --- a/rubix/telescope/base.py +++ b/rubix/telescope/base.py @@ -1,9 +1,9 @@ from typing import List, Optional, Union -from jaxtyping import Int, Float, Array, jaxtyped -from beartype import beartype as typechecker -import numpy as np import equinox as eqx +import numpy as np +from beartype import beartype as typechecker +from jaxtyping import Array, Float, Int, jaxtyped @jaxtyped(typechecker=typechecker) diff --git a/rubix/telescope/factory.py b/rubix/telescope/factory.py index 291bd1e2..79dcde28 100644 --- a/rubix/telescope/factory.py +++ b/rubix/telescope/factory.py @@ -1,17 +1,19 @@ +import os +import warnings +from typing import Optional, Union + import numpy as np +from beartype import beartype as typechecker +from jaxtyping import jaxtyped + from rubix.telescope.apertures import ( - SQUARE_APERTURE, CIRCULAR_APERTURE, HEXAGONAL_APERTURE, + SQUARE_APERTURE, ) from rubix.telescope.base import BaseTelescope from rubix.telescope.utils import calculate_wave_edges, calculate_wave_seq from rubix.utils import read_yaml -import os -import warnings -from typing import Optional, Union -from jaxtyping import jaxtyped -from beartype import beartype as typechecker PATH = os.path.dirname(os.path.abspath(__file__)) TELESCOPE_CONFIG_PATH = os.path.join(PATH, "telescopes.yaml") diff --git a/rubix/telescope/filters/__init__.py b/rubix/telescope/filters/__init__.py index ac51f92f..95fed769 100644 --- a/rubix/telescope/filters/__init__.py +++ b/rubix/telescope/filters/__init__.py @@ -1 +1 @@ -from .filters import * \ No newline at end of file +from .filters import * diff --git a/rubix/telescope/filters/filters.py b/rubix/telescope/filters/filters.py index dab1c2f6..1fe3787f 100644 --- a/rubix/telescope/filters/filters.py +++ b/rubix/telescope/filters/filters.py @@ -1,13 +1,15 @@ +import os +from typing import List, Optional, Union + import equinox as eqx import jax.numpy as jnp import matplotlib.pyplot as plt -from jaxtyping import Array, Float -from typing import List, Union, Optional -from rubix.paths import FILTERS_PATH -from rubix.logger import get_logger from astropy.table import Table from astroquery.svo_fps import SvoFps -import os +from jaxtyping import Array, Float + +from rubix.logger import get_logger +from rubix.paths import FILTERS_PATH _logger = get_logger() @@ -337,15 +339,13 @@ def _load_filter_list_for_instrument( for ID in filter_table["filterID"]: if ID.startswith(filter_prefix): # filter_data = filter_table.loc[ID] - #tmp_ID = ID.split("/")[-1] + # tmp_ID = ID.split("/")[-1] # check if the filter file is present on disk # if not, download it from the SVO Filter Profile Service # and save it to the specified path # this is needed if from the previous run the specific filters were not saved to disk or only the instrument table was saved. if not os.path.exists(f"{filter_dir}/{ID}.csv"): - _logger.info( - f"Filter file {ID}.csv not found in {filter_dir}." - ) + _logger.info(f"Filter file {ID}.csv not found in {filter_dir}.") _logger.info( f"Start downloading telescope filter files for {filter_prefix}." ) @@ -476,7 +476,9 @@ def print_filter_list_info(facility: str, instrument: Optional[str] = None): print(filter_list.info) -def print_filter_property(facility: str, filter_name: str, instrument: Optional[str] = None): +def print_filter_property( + facility: str, filter_name: str, instrument: Optional[str] = None +): """ Print the properties of a filter available for a given facility, instrument and filter name. If you want to see the list of all facilities and instruments, follow the link below: diff --git a/rubix/telescope/lsf/lsf.py b/rubix/telescope/lsf/lsf.py index 5116f025..92a2375d 100644 --- a/rubix/telescope/lsf/lsf.py +++ b/rubix/telescope/lsf/lsf.py @@ -4,9 +4,9 @@ """ import jax.numpy as jnp -from jax.scipy.signal import convolve from jax import vmap -from jaxtyping import Float, Array +from jax.scipy.signal import convolve +from jaxtyping import Array, Float def gaussian1d(x: Float[Array, " n_x"], sigma: float) -> Float[Array, " n_x"]: diff --git a/rubix/telescope/noise/noise.py b/rubix/telescope/noise/noise.py index 40646021..774ed45e 100644 --- a/rubix/telescope/noise/noise.py +++ b/rubix/telescope/noise/noise.py @@ -1,6 +1,5 @@ import jax.numpy as jnp from jax import random as jrandom - from jaxtyping import Array, Float SUPPORTED_NOISE_DISTRIBUTIONS = ["normal", "uniform"] diff --git a/rubix/telescope/psf/kernels.py b/rubix/telescope/psf/kernels.py index 59c4376c..24f3db72 100644 --- a/rubix/telescope/psf/kernels.py +++ b/rubix/telescope/psf/kernels.py @@ -1,5 +1,5 @@ import jax.numpy as jnp -from jaxtyping import Float, Array +from jaxtyping import Array, Float def gaussian_kernel_2d(m: int, n: int, sigma: float) -> Float[Array, "m n"]: diff --git a/rubix/telescope/psf/psf.py b/rubix/telescope/psf/psf.py index 3e8dc8d8..cdf8b584 100644 --- a/rubix/telescope/psf/psf.py +++ b/rubix/telescope/psf/psf.py @@ -1,7 +1,8 @@ import jax.numpy as jnp +from jax import vmap from jax.scipy.signal import convolve2d from jaxtyping import Array, Float -from jax import vmap + from .kernels import gaussian_kernel_2d diff --git a/rubix/telescope/utils.py b/rubix/telescope/utils.py index 2a571e7b..939604bc 100644 --- a/rubix/telescope/utils.py +++ b/rubix/telescope/utils.py @@ -1,10 +1,11 @@ +from typing import List, Tuple, Union + import jax.numpy as jnp import numpy as np -from rubix.cosmology.base import BaseCosmology -from typing import Tuple, List -from jaxtyping import Float, Array, Bool, Int, jaxtyped from beartype import beartype as typechecker -from typing import Union +from jaxtyping import Array, Bool, Float, Int, jaxtyped + +from rubix.cosmology.base import BaseCosmology @jaxtyped(typechecker=typechecker) diff --git a/rubix/units.py b/rubix/units.py index 6a7b1448..31b8337d 100644 --- a/rubix/units.py +++ b/rubix/units.py @@ -2,4 +2,4 @@ # Define custom units here Zsun = u.def_unit("Zsun", u.dimensionless_unscaled) -u.add_enabled_units(Zsun) \ No newline at end of file +u.add_enabled_units(Zsun) diff --git a/rubix/utils.py b/rubix/utils.py index dff53a23..07cf77d7 100644 --- a/rubix/utils.py +++ b/rubix/utils.py @@ -1,10 +1,11 @@ # Description: Utility functions for Rubix import os -from astropy.cosmology import Planck15 as cosmo -import yaml -import h5py from typing import Dict, Union +import h5py +import yaml +from astropy.cosmology import Planck15 as cosmo + def get_config(config: Union[str, Dict]) -> Dict: """ diff --git a/setup.py b/setup.py index b024da80..60684932 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,3 @@ from setuptools import setup - setup() diff --git a/tests/test_apertures.py b/tests/test_apertures.py index f97080e4..c6d1e257 100644 --- a/tests/test_apertures.py +++ b/tests/test_apertures.py @@ -1,10 +1,11 @@ -import pytest # type: ignore # noqa import jax.numpy as jnp import numpy as np +import pytest # type: ignore # noqa + from rubix.telescope.apertures import ( + CIRCULAR_APERTURE, HEXAGONAL_APERTURE, SQUARE_APERTURE, - CIRCULAR_APERTURE, ) diff --git a/tests/test_core_cosmology.py b/tests/test_core_cosmology.py index a7eb0cbf..c0054ab2 100644 --- a/tests/test_core_cosmology.py +++ b/tests/test_core_cosmology.py @@ -1,6 +1,7 @@ import pytest -from rubix.cosmology import RubixCosmology, PLANCK15 + from rubix.core.cosmology import get_cosmology +from rubix.cosmology import PLANCK15, RubixCosmology def test_get_cosmology_planck15(): diff --git a/tests/test_core_data.py b/tests/test_core_data.py index 6b37f2b1..70082b77 100644 --- a/tests/test_core_data.py +++ b/tests/test_core_data.py @@ -1,15 +1,19 @@ -from unittest.mock import MagicMock, Mock, patch, call +from unittest.mock import MagicMock, Mock, call, patch import jax import jax.numpy as jnp + from rubix.core.data import ( + Galaxy, + GasData, + RubixData, + StarsData, convert_to_rubix, + get_reshape_data, + get_rubix_data, prepare_input, reshape_array, - get_rubix_data, - get_reshape_data, ) -from rubix.core.data import RubixData, Galaxy, StarsData, GasData # Mock configuration for tests config_dict = { @@ -139,7 +143,6 @@ def test_prepare_input(mock_center_particles, mock_path_join): ) - @patch("rubix.core.data.os.path.join") @patch("rubix.core.data.center_particles") @patch("rubix.core.data.get_logger") diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 2f1f391b..4dd948fc 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -2,16 +2,16 @@ import jax.numpy as jnp import numpy as np -from rubix.spectra.ifu import resample_spectrum -from rubix.core.data import reshape_array, RubixData, Galaxy, StarsData, GasData -from rubix.core.ssp import get_ssp +from rubix.core.data import Galaxy, GasData, RubixData, StarsData, reshape_array from rubix.core.ifu import ( get_calculate_spectra, - get_scale_spectrum_by_mass, - get_resample_spectrum_vmap, - get_resample_spectrum_pmap, get_doppler_shift_and_resampling, + get_resample_spectrum_pmap, + get_resample_spectrum_vmap, + get_scale_spectrum_by_mass, ) +from rubix.core.ssp import get_ssp +from rubix.spectra.ifu import resample_spectrum RTOL = 1e-4 ATOL = 1e-6 diff --git a/tests/test_core_lsf.py b/tests/test_core_lsf.py index df915496..ff043736 100644 --- a/tests/test_core_lsf.py +++ b/tests/test_core_lsf.py @@ -1,6 +1,7 @@ +import jax.numpy as jnp import pytest + from rubix.core.lsf import get_convolve_lsf -import jax.numpy as jnp def test_get_convolve_lsf_missing_lsf_key(): diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index 71f9432c..1f6430a6 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -1,12 +1,13 @@ -import pytest -from unittest.mock import patch, MagicMock +import os # noqa +from unittest.mock import MagicMock, patch + import jax.numpy as jnp +import pytest + from rubix.core.pipeline import RubixPipeline from rubix.spectra.ssp.grid import SSPGrid from rubix.telescope.base import BaseTelescope -import os # noqa - # Dummy data functions def dummy_get_rubix_data(config): @@ -64,14 +65,12 @@ def setup_environment(monkeypatch): }, }, "ssp": { - "template": { - "name": "BruzualCharlot2003" - }, + "template": {"name": "BruzualCharlot2003"}, "dust": { - "extinction_model": "Cardelli89", #"Gordon23", - "dust_to_gas_ratio": 0.01, # need to check Remyer's paper - "dust_to_metals_ratio": 0.4, # do we need this ratio if we set the dust_to_gas_ratio? - "dust_grain_density": 3.5, # g/cm^3 #check this value + "extinction_model": "Cardelli89", # "Gordon23", + "dust_to_gas_ratio": 0.01, # need to check Remyer's paper + "dust_to_metals_ratio": 0.4, # do we need this ratio if we set the dust_to_gas_ratio? + "dust_grain_density": 3.5, # g/cm^3 #check this value "Rv": 3.1, }, }, diff --git a/tests/test_core_psf.py b/tests/test_core_psf.py index a18569ea..51c58326 100644 --- a/tests/test_core_psf.py +++ b/tests/test_core_psf.py @@ -1,4 +1,5 @@ import pytest + from rubix.core.psf import get_convolve_psf diff --git a/tests/test_core_rotation.py b/tests/test_core_rotation.py index 96aa154c..5e74f8f8 100644 --- a/tests/test_core_rotation.py +++ b/tests/test_core_rotation.py @@ -1,4 +1,5 @@ import pytest + from rubix.core.rotation import get_galaxy_rotation diff --git a/tests/test_core_ssp.py b/tests/test_core_ssp.py index 1b97fdf7..ccbe950a 100644 --- a/tests/test_core_ssp.py +++ b/tests/test_core_ssp.py @@ -1,13 +1,14 @@ +import jax.numpy as jnp import pytest + +from rubix import config from rubix.core.data import reshape_array from rubix.core.ssp import ( get_lookup_interpolation, - get_ssp, - get_lookup_interpolation_vmap, get_lookup_interpolation_pmap, + get_lookup_interpolation_vmap, + get_ssp, ) -from rubix import config -import jax.numpy as jnp ssp_config = config["ssp"] supported_templates = ssp_config["templates"] diff --git a/tests/test_core_telescope.py b/tests/test_core_telescope.py index 07f1df0a..5767c6c2 100644 --- a/tests/test_core_telescope.py +++ b/tests/test_core_telescope.py @@ -1,13 +1,15 @@ +from typing import cast +from unittest.mock import MagicMock, patch + +import jax.numpy as jnp import pytest + from rubix.core.telescope import ( + get_spatial_bin_edges, get_spaxel_assignment, get_telescope, - get_spatial_bin_edges, ) from rubix.telescope.base import BaseTelescope -from unittest.mock import patch, MagicMock -from typing import cast -import jax.numpy as jnp class MockRubixData: diff --git a/tests/test_cosmology.py b/tests/test_cosmology.py index 185d0a8d..fa8b05a3 100644 --- a/tests/test_cosmology.py +++ b/tests/test_cosmology.py @@ -1,6 +1,7 @@ import pytest -from jax import numpy as jnp from astropy.cosmology import Planck15 as astropy_cosmo +from jax import numpy as jnp + from rubix.cosmology import PLANCK15 as rubix_cosmo # Define the cosmological parameters similar to the ones used in the BaseCosmology class diff --git a/tests/test_debug.py b/tests/test_debug.py index 2e7a7cc5..2cad7efc 100644 --- a/tests/test_debug.py +++ b/tests/test_debug.py @@ -1,9 +1,10 @@ import h5py import jax.numpy as jnp -from rubix.debug import ( - random_data, + +from rubix.debug import ( # Adjust the import based on your actual file structure create_dummy_rubix, -) # Adjust the import based on your actual file structure + random_data, +) from rubix.utils import print_hdf5_file_structure diff --git a/tests/test_dust_classes.py b/tests/test_dust_classes.py index ccc8ddc0..f064eccb 100644 --- a/tests/test_dust_classes.py +++ b/tests/test_dust_classes.py @@ -1,8 +1,15 @@ +import jax.numpy as jnp import pytest -from rubix.spectra.dust.generic_models import PowerLaw1d, Polynomial1d, Drude1d, _modified_drude, FM90 + from rubix.spectra.dust.extinction_models import Cardelli89, Gordon23 +from rubix.spectra.dust.generic_models import ( + FM90, + Drude1d, + Polynomial1d, + PowerLaw1d, + _modified_drude, +) -import jax.numpy as jnp def test_PowerLaw1d(): x = jnp.array([1.0, 2.0, 3.0]) @@ -13,6 +20,7 @@ def test_PowerLaw1d(): result = PowerLaw1d(x, amplitude, x_0, alpha) assert jnp.allclose(result, expected), f"Expected {expected}, but got {result}" + def test_Polynomial1d(): x = jnp.array([1.0, 2.0, 3.0]) coeffs = jnp.array([1.0, 2.0, 3.0]) @@ -20,6 +28,7 @@ def test_Polynomial1d(): result = Polynomial1d(x, coeffs) assert jnp.allclose(result, expected), f"Expected {expected}, but got {result}" + def test_Polynomial1d_single_coefficient(): x = jnp.array([1.0, 2.0, 3.0]) coeffs = jnp.array([1.0]) @@ -27,24 +36,27 @@ def test_Polynomial1d_single_coefficient(): result = Polynomial1d(x, coeffs) assert jnp.allclose(result, expected), f"Expected {expected}, but got {result}" + def test_Drude1d(): x = jnp.array([1.0, 2.0, 3.0]) amplitude = 1.0 x_0 = 1.0 fwhm = 1.0 - expected = jnp.array([1.0, 0.30769232, 0.12328766]) + expected = jnp.array([1.0, 0.30769232, 0.12328766]) result = Drude1d(x, amplitude, x_0, fwhm) assert jnp.allclose(result, expected), f"Expected {expected}, but got {result}" + def test_Drude1d_value_error(): x = jnp.array([1.0, 2.0, 3.0]) amplitude = 1.0 x_0 = 0.0 fwhm = 1.0 - expected = jnp.array([1.0, 0.30769232, 0.12328766]) + expected = jnp.array([1.0, 0.30769232, 0.12328766]) with pytest.raises(ValueError, match="0 is not an allowed value for x_0"): result = Drude1d(x, amplitude, x_0, fwhm) + def test_modified_drude(): x = jnp.array([1.0, 2.0, 3.0]) scale = 1.0 @@ -55,6 +67,7 @@ def test_modified_drude(): result = _modified_drude(x, scale, x_o, gamma_o, asym) assert jnp.allclose(result, expected), f"Expected {expected}, but got {result}" + def test_FM90(): x = jnp.array([4.0, 5.0, 6.0]) C1 = 0.10 @@ -63,10 +76,11 @@ def test_FM90(): C4 = 0.41 xo = 4.59 gamma = 0.95 - expected = jnp.array([4.1879544, 5.723751, 4.7574277]) + expected = jnp.array([4.1879544, 5.723751, 4.7574277]) result = FM90(x, C1, C2, C3, C4, xo, gamma) assert jnp.allclose(result, expected), f"Expected {expected}, but got {result}" + def test_FM90_value_errors(): x = jnp.array([4.0, 5.0, 6.0]) @@ -94,26 +108,31 @@ def test_FM90_value_errors(): with pytest.raises(ValueError, match="gamma is out of bounds: 0.1"): FM90(x, C1=0.0, C2=0.5, C3=3.0, C4=0.5, xo=4.5, gamma=0.1) + def test_cardelli89_evaluate(): # Test with a sample wavelength array wave = jnp.array([0.5, 1.0, 2.0, 3.0, 5.0, 8.0, 10.0]) model = Cardelli89(Rv=3.1) result = model.evaluate(wave) - + # Check the shape of the result assert result.shape == wave.shape - + # Check the values are within expected range assert jnp.all(result >= 0) assert jnp.all(result <= 10) + def test_cardelli89_no_AV_noEbv(): # Test with a sample wavelength array wave = jnp.array([0.5, 1.0, 2.0, 3.0, 5.0, 8.0, 10.0]) model = Cardelli89(Rv=3.1) - with pytest.raises(ValueError, match="neither Av or Ebv passed, one of them is required!"): + with pytest.raises( + ValueError, match="neither Av or Ebv passed, one of them is required!" + ): result = model.extinguish(wave) + def test_cardelli89_no_AV(): # Test with a sample wavelength array wave = jnp.array([0.5, 1.0, 2.0, 3.0, 5.0, 8.0, 10.0]) @@ -122,24 +141,30 @@ def test_cardelli89_no_AV(): # Calculate expected extinction values Av = 3.1 * 1.0 # Since Ebv=1.0, Av = Rv * Ebv = 3.1 * 1.0 = 3.1 - expected = model.evaluate(wave) * Av # Since Ebv=1.0, Av = Rv * Ebv = 3.1 * 1.0 = 3.1 + expected = ( + model.evaluate(wave) * Av + ) # Since Ebv=1.0, Av = Rv * Ebv = 3.1 * 1.0 = 3.1 expected_extinction = jnp.power(10.0, -0.4 * expected) - assert jnp.allclose(result, expected_extinction)#, f"Expected {expected_extinction}, but got {result}" + assert jnp.allclose( + result, expected_extinction + ) # , f"Expected {expected_extinction}, but got {result}" + def test_gordon23_evaluate(): # Test with a sample wavelength array wave = jnp.array([0.1, 0.3, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0, 30.0]) model = Gordon23(Rv=3.1) result = model.evaluate(wave) - + # Check the shape of the result assert result.shape == wave.shape - + # Check the values are within expected range assert jnp.all(result >= 0) assert jnp.all(result <= 10) + """ def test_cardelli89_invalid_rv(): # Test with an invalid Rv value @@ -164,4 +189,4 @@ def test_gordon23_wave_out_of_range(): wave = jnp.array([0.05, 40.0]) # Out of range wavelengths with pytest.raises(ValueError): model.evaluate(wave) -""" \ No newline at end of file +""" diff --git a/tests/test_dust_extinction.py b/tests/test_dust_extinction.py index 1a3bba5e..eccbad25 100644 --- a/tests/test_dust_extinction.py +++ b/tests/test_dust_extinction.py @@ -1,13 +1,16 @@ -import pytest from unittest.mock import MagicMock, patch -from rubix.core.dust import get_extinction + +import jax.numpy as jnp +import pytest + from rubix.core.data import RubixData -from rubix.spectra.dust.dust_extinction import apply_spaxel_extinction -from rubix.spectra.dust.dust_extinction import calculate_dust_to_gas_ratio, apply_spaxel_extinction -from rubix.spectra.dust.dust_extinction import apply_spaxel_extinction +from rubix.core.dust import get_extinction +from rubix.spectra.dust.dust_extinction import ( + apply_spaxel_extinction, + calculate_dust_to_gas_ratio, +) from rubix.spectra.dust.helpers import poly_map_domain -import jax.numpy as jnp @pytest.fixture def mock_config(): @@ -16,7 +19,7 @@ def mock_config(): "constants": { "MASS_OF_PROTON": 1.6726219e-24, "MSUN_TO_GRAMS": 1.989e33, - "KPC_TO_CM": 3.086e21 + "KPC_TO_CM": 3.086e21, }, "ssp": { "dust": { @@ -24,18 +27,21 @@ def mock_config(): "extinction_model": "Cardelli89", "Rv": 3.1, "dust_to_gas_model": "power law slope free", - "Xco": "MW" + "Xco": "MW", } - } + }, } + @pytest.fixture def mock_rubixdata(): class MockGas: def __init__(self): self.coords = jnp.array([[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]]) self.pixel_assignment = jnp.array([[0, 1]]) - self.metals = jnp.array([[[0.01, 0.02, 0.03, 0.04, 0.05], [0.06, 0.07, 0.08, 0.09, 0.1]]]) + self.metals = jnp.array( + [[[0.01, 0.02, 0.03, 0.04, 0.05], [0.06, 0.07, 0.08, 0.09, 0.1]]] + ) self.mass = jnp.array([[1.0, 2.0]]) class MockStars: @@ -52,16 +58,20 @@ def __init__(self): return MockRubixData() + def test_spaxel_extinction_Cardelli(mock_config, mock_rubixdata): wavelength = jnp.array([5000.0, 6000.0]) n_spaxels = 2 spaxel_area = jnp.array([1.0, 1.0]) - result = apply_spaxel_extinction(mock_config, mock_rubixdata, wavelength, n_spaxels, spaxel_area) + result = apply_spaxel_extinction( + mock_config, mock_rubixdata, wavelength, n_spaxels, spaxel_area + ) assert result.shape == (1, 2, 2) assert jnp.all(result >= 0) + @pytest.fixture def mock_config(): return { @@ -69,7 +79,7 @@ def mock_config(): "constants": { "MASS_OF_PROTON": 1.6726219e-24, "MSUN_TO_GRAMS": 1.989e33, - "KPC_TO_CM": 3.086e21 + "KPC_TO_CM": 3.086e21, }, "ssp": { "dust": { @@ -77,21 +87,25 @@ def mock_config(): "extinction_model": "Gordon23", "Rv": 3.1, "dust_to_gas_model": "power law slope free", - "Xco": "MW" + "Xco": "MW", } - } + }, } + def test_spaxel_extinction_Gordon(mock_config, mock_rubixdata): wavelength = jnp.array([5000.0, 6000.0]) n_spaxels = 2 spaxel_area = jnp.array([1.0, 1.0]) - result = apply_spaxel_extinction(mock_config, mock_rubixdata, wavelength, n_spaxels, spaxel_area) + result = apply_spaxel_extinction( + mock_config, mock_rubixdata, wavelength, n_spaxels, spaxel_area + ) assert result.shape == (1, 2, 2) assert jnp.all(result >= 0) + @pytest.fixture def mock_config(): return { @@ -99,7 +113,7 @@ def mock_config(): "constants": { "MASS_OF_PROTON": 1.6726219e-24, "MSUN_TO_GRAMS": 1.989e33, - "KPC_TO_CM": 3.086e21 + "KPC_TO_CM": 3.086e21, }, "ssp": { "dust": { @@ -107,18 +121,21 @@ def mock_config(): "extinction_model": "Cardelli89", "Rv": 3.1, "dust_to_gas_model": "power law slope free", - "Xco": "MW" + "Xco": "MW", } - } + }, } + @pytest.fixture def mock_rubixdata(): class MockGas: def __init__(self): self.coords = jnp.array([[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]]) self.pixel_assignment = jnp.array([[0, 1]]) - self.metals = jnp.array([[[0.01, 0.02, 0.03, 0.04, 0.05], [0.06, 0.07, 0.08, 0.09, 0.1]]]) + self.metals = jnp.array( + [[[0.01, 0.02, 0.03, 0.04, 0.05], [0.06, 0.07, 0.08, 0.09, 0.1]]] + ) self.mass = jnp.array([[1.0, 2.0]]) class MockStars: @@ -135,6 +152,7 @@ def __init__(self): return MockRubixData() + def test_calculate_dust_to_gas_ratio_power_law_slope_free_MW(): gas_metallicity = jnp.array([8.0, 8.5]) model = "power law slope free" @@ -143,6 +161,7 @@ def test_calculate_dust_to_gas_ratio_power_law_slope_free_MW(): assert result.shape == (2,) assert jnp.all(result >= 0) + def test_calculate_dust_to_gas_ratio_broken_power_law_fit_MW(): gas_metallicity = jnp.array([8.0, 8.5]) model = "broken power law fit" @@ -151,6 +170,7 @@ def test_calculate_dust_to_gas_ratio_broken_power_law_fit_MW(): assert result.shape == (2,) assert jnp.all(result >= 0) + def test_calculate_dust_to_gas_ratio_power_law_slope_free_Z(): gas_metallicity = jnp.array([8.0, 8.5]) model = "power law slope free" @@ -159,6 +179,7 @@ def test_calculate_dust_to_gas_ratio_power_law_slope_free_Z(): assert result.shape == (2,) assert jnp.all(result >= 0) + def test_calculate_dust_to_gas_ratio_broken_power_law_fit_Z(): gas_metallicity = jnp.array([8.0, 8.5]) model = "broken power law fit" @@ -167,40 +188,52 @@ def test_calculate_dust_to_gas_ratio_broken_power_law_fit_Z(): assert result.shape == (2,) assert jnp.all(result >= 0) + def test_invalid_extinction_model(mock_config, mock_rubixdata): # Modify the config to use an invalid extinction model mock_config["ssp"]["dust"]["extinction_model"] = "InvalidModel" - + wavelength = jnp.array([5000.0, 6000.0]) n_spaxels = 2 spaxel_area = jnp.array([1.0, 1.0]) - - with pytest.raises(ValueError, match="Extinction model 'InvalidModel' is not available. Choose from"): - apply_spaxel_extinction(mock_config, mock_rubixdata, wavelength, n_spaxels, spaxel_area) + + with pytest.raises( + ValueError, + match="Extinction model 'InvalidModel' is not available. Choose from", + ): + apply_spaxel_extinction( + mock_config, mock_rubixdata, wavelength, n_spaxels, spaxel_area + ) def test_get_extinction_raises_value_error_for_missing_dust_key(): - config = { - "ssp": {}, - "galaxy": {"dist_z": 0.1} - } - with pytest.raises(ValueError, match="Dust configuration not found in config file."): + config = {"ssp": {}, "galaxy": {"dist_z": 0.1}} + with pytest.raises( + ValueError, match="Dust configuration not found in config file." + ): get_extinction(config) + def test_get_extinction_raises_value_error_for_missing_extinction_model(): - config = { - "ssp": {"dust": {}}, - "galaxy": {"dist_z": 0.1} - } - with pytest.raises(ValueError, match="Extinction model not found in dust configuration."): + config = {"ssp": {"dust": {}}, "galaxy": {"dist_z": 0.1}} + with pytest.raises( + ValueError, match="Extinction model not found in dust configuration." + ): get_extinction(config) -@patch('rubix.core.dust.get_telescope') -@patch('rubix.core.dust.get_cosmology') -@patch('rubix.core.dust.calculate_spatial_bin_edges') -@patch('rubix.core.dust.apply_spaxel_extinction') -@patch('rubix.core.dust.get_logger') -def test_get_extinction_applies_dust_extinction(mock_get_logger, mock_apply_spaxel_extinction, mock_calculate_spatial_bin_edges, mock_get_cosmology, mock_get_telescope): + +@patch("rubix.core.dust.get_telescope") +@patch("rubix.core.dust.get_cosmology") +@patch("rubix.core.dust.calculate_spatial_bin_edges") +@patch("rubix.core.dust.apply_spaxel_extinction") +@patch("rubix.core.dust.get_logger") +def test_get_extinction_applies_dust_extinction( + mock_get_logger, + mock_apply_spaxel_extinction, + mock_calculate_spatial_bin_edges, + mock_get_cosmology, + mock_get_telescope, +): mock_logger = MagicMock() mock_get_logger.return_value = mock_logger @@ -213,7 +246,7 @@ def test_get_extinction_applies_dust_extinction(mock_get_logger, mock_apply_spax config = { "ssp": {"dust": {"extinction_model": "some_model"}}, - "galaxy": {"dist_z": 0.1} + "galaxy": {"dist_z": 0.1}, } rubixdata = MagicMock(spec=RubixData) @@ -222,6 +255,10 @@ def test_get_extinction_applies_dust_extinction(mock_get_logger, mock_apply_spax calculate_extinction = get_extinction(config) result = calculate_extinction(rubixdata) - mock_logger.info.assert_called_with("Applying dust extinction to the spaxel data...") - mock_apply_spaxel_extinction.assert_called_with(config, rubixdata, [5000, 6000, 7000], 4, 1.0) - assert result == rubixdata \ No newline at end of file + mock_logger.info.assert_called_with( + "Applying dust extinction to the spaxel data..." + ) + mock_apply_spaxel_extinction.assert_called_with( + config, rubixdata, [5000, 6000, 7000], 4, 1.0 + ) + assert result == rubixdata diff --git a/tests/test_factory.py b/tests/test_factory.py index 3d2b6666..96a0f9a5 100644 --- a/tests/test_factory.py +++ b/tests/test_factory.py @@ -1,5 +1,7 @@ +from unittest.mock import MagicMock, patch + import pytest -from unittest.mock import patch, MagicMock + from rubix.galaxy.input_handler.factory import get_input_handler diff --git a/tests/test_galaxy_alignment.py b/tests/test_galaxy_alignment.py index d8af9e84..173dd149 100644 --- a/tests/test_galaxy_alignment.py +++ b/tests/test_galaxy_alignment.py @@ -1,15 +1,16 @@ -import pytest +import jax.numpy as jnp import numpy as np -from rubix.galaxy.alignment import center_particles +import pytest + from rubix.galaxy.alignment import ( - moment_of_inertia_tensor, - rotation_matrix_from_inertia_tensor, apply_init_rotation, - euler_rotation_matrix, apply_rotation, + center_particles, + euler_rotation_matrix, + moment_of_inertia_tensor, + rotate_galaxy, + rotation_matrix_from_inertia_tensor, ) -from rubix.galaxy.alignment import rotate_galaxy -import jax.numpy as jnp class MockRubixData: diff --git a/tests/test_illustris_handler.py b/tests/test_illustris_handler.py index c50ae905..73cf9073 100644 --- a/tests/test_illustris_handler.py +++ b/tests/test_illustris_handler.py @@ -1,6 +1,8 @@ -import pytest +from unittest.mock import MagicMock, patch + import numpy as np -from unittest.mock import patch, MagicMock +import pytest + from rubix.galaxy import IllustrisHandler from rubix.utils import SFTtoAge diff --git a/tests/test_input_handler.py b/tests/test_input_handler.py index be5b6b7b..a7f2b80e 100644 --- a/tests/test_input_handler.py +++ b/tests/test_input_handler.py @@ -1,7 +1,8 @@ -import pytest -from rubix.galaxy import BaseHandler import h5py +import pytest + from rubix import config +from rubix.galaxy import BaseHandler class ConcreteInputHandler(BaseHandler): diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index 61f5005e..f6267ee1 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -1,12 +1,14 @@ -from rubix.pipeline import linear_pipeline as lp -from rubix.utils import read_yaml -import pytest -from pathlib import Path from copy import deepcopy +from pathlib import Path + import jax.numpy as jnp -from jax import make_jaxpr, jit +import pytest +from jax import jit, make_jaxpr from jax.tree_util import Partial +from rubix.pipeline import linear_pipeline as lp +from rubix.utils import read_yaml + # helper stuff that we need def add(x, s: float = 0.0): diff --git a/tests/test_pynbody_handler.py b/tests/test_pynbody_handler.py index 34796029..b8656811 100644 --- a/tests/test_pynbody_handler.py +++ b/tests/test_pynbody_handler.py @@ -1,8 +1,11 @@ -import pytest from unittest.mock import MagicMock, patch + import numpy as np +import pytest + from rubix.galaxy.input_handler.pynbody import PynbodyHandler + @pytest.fixture def mock_config(): """Mocked configuration for PynbodyHandler.""" @@ -37,12 +40,19 @@ def mock_config(): "galaxy": {"dist_z": 0.1}, } + @pytest.fixture def mock_simulation(): """Mocked simulation object that mimics a pynbody SimSnap (stars, gas, dm).""" mock_sim = MagicMock() - mock_sim.stars.loadable_keys.return_value = ["pos", "mass", "vel", "metallicity", "age"] + mock_sim.stars.loadable_keys.return_value = [ + "pos", + "mass", + "vel", + "metallicity", + "age", + ] mock_sim.gas.loadable_keys.return_value = ["density", "temperature"] mock_sim.dm.loadable_keys.return_value = ["mass"] @@ -59,9 +69,7 @@ def mock_simulation(): "temperature": np.array([100.0, 200.0, 300.0]), } - dm_arrays = { - "mass": np.array([10.0, 20.0, 30.0]) - } + dm_arrays = {"mass": np.array([10.0, 20.0, 30.0])} def star_getitem(key): return star_arrays[key] @@ -86,6 +94,7 @@ def dm_getitem(key): return mock_sim + @pytest.fixture def handler_with_mock_data(mock_simulation, mock_config): with patch("pynbody.load", return_value=mock_simulation): @@ -99,15 +108,18 @@ def handler_with_mock_data(mock_simulation, mock_config): ) return handler + def test_pynbody_handler_initialization(handler_with_mock_data): """Test initialization of PynbodyHandler.""" assert handler_with_mock_data is not None + def test_load_data(handler_with_mock_data): """Test if data is loaded correctly.""" data = handler_with_mock_data.get_particle_data() assert "stars" in data + def test_get_galaxy_data(handler_with_mock_data): """Test retrieval of galaxy data.""" galaxy_data = handler_with_mock_data.get_galaxy_data() @@ -122,6 +134,7 @@ def test_get_galaxy_data(handler_with_mock_data): assert galaxy_data["center"] == expected_center assert "halfmassrad_stars" in galaxy_data + def test_get_units(handler_with_mock_data): """Test if units are correctly returned.""" units = handler_with_mock_data.get_units() @@ -129,6 +142,7 @@ def test_get_units(handler_with_mock_data): assert "gas" in units assert "dm" in units + def test_gas_data_load(handler_with_mock_data): """Test loading of gas data.""" data = handler_with_mock_data.get_particle_data() @@ -136,6 +150,7 @@ def test_gas_data_load(handler_with_mock_data): assert "density" in data["gas"] assert "temperature" in data["gas"] + def test_stars_data_load(handler_with_mock_data): """Test loading of stars data.""" data = handler_with_mock_data.get_particle_data() diff --git a/tests/test_spectra_ifu.py b/tests/test_spectra_ifu.py index f7d6f759..3d6d9d72 100644 --- a/tests/test_spectra_ifu.py +++ b/tests/test_spectra_ifu.py @@ -1,16 +1,17 @@ -import pytest -import numpy as np import jax.numpy as jnp +import numpy as np +import pytest + from rubix.spectra.ifu import ( + _get_velocity_component_multiple, + _get_velocity_component_single, + calculate_cube, calculate_diff, convert_luminoisty_to_flux, - _get_velocity_component_single, - _get_velocity_component_multiple, - resample_spectrum, cosmological_doppler_shift, - velocity_doppler_shift, get_velocity_component, - calculate_cube, + resample_spectrum, + velocity_doppler_shift, ) # Assuming the functions are imported from the module diff --git a/tests/test_ssp_factory.py b/tests/test_ssp_factory.py index a82fad40..95eb64fd 100644 --- a/tests/test_ssp_factory.py +++ b/tests/test_ssp_factory.py @@ -1,11 +1,12 @@ -import pytest +import sys +from copy import deepcopy +from unittest.mock import MagicMock, patch + import numpy as np -from unittest.mock import patch, MagicMock -from rubix.spectra.ssp.factory import get_ssp_template -from rubix.spectra.ssp.factory import HDF5SSPGrid, pyPipe3DSSPGrid +import pytest + from rubix.paths import TEMPLATE_PATH -from copy import deepcopy -import sys +from rubix.spectra.ssp.factory import HDF5SSPGrid, get_ssp_template, pyPipe3DSSPGrid # Fixture to reset the configuration after each test @@ -78,9 +79,9 @@ def test_get_ssp_template_existing_template(): template = get_ssp_template(template_name) template_class_name = config["ssp"]["templates"][template_name]["name"] assert template.__class__.__name__ == template_class_name - assert mock_write_fsps_data_to_disk.call_count <= 1, ( - f"Expected at most 1 call to 'write_fsps_data_to_disk', but got {mock_write_fsps_data_to_disk.call_count}" - ) + assert ( + mock_write_fsps_data_to_disk.call_count <= 1 + ), f"Expected at most 1 call to 'write_fsps_data_to_disk', but got {mock_write_fsps_data_to_disk.call_count}" def test_get_ssp_template_existing_template_BC03(): diff --git a/tests/test_ssp_fsps.py b/tests/test_ssp_fsps.py index ffcf5a82..26fb5720 100644 --- a/tests/test_ssp_fsps.py +++ b/tests/test_ssp_fsps.py @@ -1,12 +1,14 @@ -import pytest -import numpy as np -from unittest.mock import patch -from rubix.spectra.ssp.grid import SSPGrid -from rubix.spectra.ssp.fsps_grid import write_fsps_data_to_disk -import sys import os -import h5py +import sys from importlib import reload +from unittest.mock import patch + +import h5py +import numpy as np +import pytest + +from rubix.spectra.ssp.fsps_grid import write_fsps_data_to_disk +from rubix.spectra.ssp.grid import SSPGrid # Mock the fsps.StellarPopulation class diff --git a/tests/test_telescope_factory.py b/tests/test_telescope_factory.py index 2947a99d..81a9caf7 100644 --- a/tests/test_telescope_factory.py +++ b/tests/test_telescope_factory.py @@ -1,18 +1,18 @@ +from unittest.mock import MagicMock, patch + +import jax +import jax.numpy as jnp +import numpy as np import pytest -from unittest.mock import patch, MagicMock -from rubix.telescope.base import BaseTelescope +import yaml + from rubix.telescope.apertures import ( - SQUARE_APERTURE, CIRCULAR_APERTURE, HEXAGONAL_APERTURE, + SQUARE_APERTURE, ) -from rubix.telescope.factory import ( - TelescopeFactory, -) -import numpy as np -import yaml -import jax -import jax.numpy as jnp +from rubix.telescope.base import BaseTelescope +from rubix.telescope.factory import TelescopeFactory jax.config.update("jax_platform_name", "cpu") diff --git a/tests/test_telescope_filters.py b/tests/test_telescope_filters.py index 25f6c072..424579a1 100644 --- a/tests/test_telescope_filters.py +++ b/tests/test_telescope_filters.py @@ -1,25 +1,24 @@ +import os +from unittest.mock import MagicMock, mock_open, patch + +import jax.numpy as jnp +import matplotlib +import matplotlib.pyplot as plt import pytest -from unittest.mock import MagicMock, patch, mock_open +from astropy.table import Table + from rubix.telescope.filters.filters import ( Filter, FilterCurves, + _load_filter_list_for_instrument, convolve_filter_with_spectra, load_filter, - save_filters, print_filter_list, print_filter_list_info, print_filter_property, - _load_filter_list_for_instrument, + save_filters, ) -import os -from astropy.table import Table - -import jax.numpy as jnp - -import matplotlib -import matplotlib.pyplot as plt - # Use the Agg backend for testing to avoid opening a figure window matplotlib.use("Agg") diff --git a/tests/test_telescope_lsf.py b/tests/test_telescope_lsf.py index cd12cfd7..1e2b24f9 100644 --- a/tests/test_telescope_lsf.py +++ b/tests/test_telescope_lsf.py @@ -1,4 +1,5 @@ import jax.numpy as jnp + from rubix.telescope.lsf.lsf import apply_lsf diff --git a/tests/test_telescope_noise.py b/tests/test_telescope_noise.py index 4add94de..5a138bc0 100644 --- a/tests/test_telescope_noise.py +++ b/tests/test_telescope_noise.py @@ -1,6 +1,7 @@ -import pytest import jax.numpy as jnp import jax.random as jrandom +import pytest + from rubix.telescope.noise.noise import calculate_noise_cube, sample_noise diff --git a/tests/test_telescope_psf.py b/tests/test_telescope_psf.py index 3e0ede1b..af230430 100644 --- a/tests/test_telescope_psf.py +++ b/tests/test_telescope_psf.py @@ -1,9 +1,10 @@ -import pytest -from rubix.telescope.psf.psf import get_psf_kernel, apply_psf -import numpy as np import jax.numpy as jnp +import numpy as np +import pytest from jax.scipy.signal import convolve2d +from rubix.telescope.psf.psf import apply_psf, get_psf_kernel + def test_get_psf_kernel_gaussian(): m, n = 3, 3 diff --git a/tests/test_telescope_psf_kernels.py b/tests/test_telescope_psf_kernels.py index 04fdccef..807d067e 100644 --- a/tests/test_telescope_psf_kernels.py +++ b/tests/test_telescope_psf_kernels.py @@ -1,4 +1,5 @@ import jax.numpy as jnp + from rubix.telescope.psf.kernels import gaussian_kernel_2d diff --git a/tests/test_telescope_utils.py b/tests/test_telescope_utils.py index 98d9c6ef..f13a6e00 100644 --- a/tests/test_telescope_utils.py +++ b/tests/test_telescope_utils.py @@ -1,14 +1,15 @@ -from rubix.telescope.utils import ( - square_spaxel_assignment, - mask_particles_outside_aperture, - calculate_spatial_bin_edges, -) +from unittest.mock import MagicMock + import jax import jax.numpy as jnp -from unittest.mock import MagicMock import numpy as np from rubix.cosmology.base import BaseCosmology +from rubix.telescope.utils import ( + calculate_spatial_bin_edges, + mask_particles_outside_aperture, + square_spaxel_assignment, +) # enfrce that jax uses cpu only diff --git a/tests/test_transformer.py b/tests/test_transformer.py index 28f11488..6e830f35 100644 --- a/tests/test_transformer.py +++ b/tests/test_transformer.py @@ -1,8 +1,9 @@ -from rubix.pipeline import transformer as pt import jax.numpy as jnp -from jax import random, jit, make_jaxpr -from jax.errors import TracerBoolConversionError import pytest +from jax import jit, make_jaxpr, random +from jax.errors import TracerBoolConversionError + +from rubix.pipeline import transformer as pt def func( diff --git a/tests/test_units.py b/tests/test_units.py index 561c4545..f4814e22 100644 --- a/tests/test_units.py +++ b/tests/test_units.py @@ -1,6 +1,8 @@ -from rubix.units import Zsun import astropy.units as u +from rubix.units import Zsun + + def test_zsun_unit(): assert str(Zsun) == "Zsun" - assert u.Unit("Zsun") == Zsun \ No newline at end of file + assert u.Unit("Zsun") == Zsun diff --git a/tests/test_utils.py b/tests/test_utils.py index 7f32c682..81fbf275 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,16 +1,17 @@ +import h5py +import numpy as np import pytest # type: ignore # noqa +import yaml +from astropy.cosmology import Planck15 as cosmo + from rubix.utils import ( - convert_values_to_physical, SFTtoAge, + convert_values_to_physical, + get_config, + load_galaxy_data, print_hdf5_file_structure, read_yaml, - load_galaxy_data, - get_config, ) -import yaml -from astropy.cosmology import Planck15 as cosmo -import h5py -import numpy as np def test_convert_values_to_physical(): From aabcde044bdebcfe355ac1f8a0ebe0562f012055 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 28 May 2025 09:49:59 +0200 Subject: [PATCH 27/76] Implement review comment: offset = (_wave[1] - _wave[0]) / 2. --- rubix/spectra/ssp/fsps_grid.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/rubix/spectra/ssp/fsps_grid.py b/rubix/spectra/ssp/fsps_grid.py index e76e10f6..210e97d0 100644 --- a/rubix/spectra/ssp/fsps_grid.py +++ b/rubix/spectra/ssp/fsps_grid.py @@ -111,7 +111,8 @@ def retrieve_ssp_data_from_fsps( _wave, _fluxes = sp.get_spectrum(zmet=zmet, tage=tage, peraa=peraa) spectrum_collector.append(_fluxes) ssp_wave = np.array(_wave) - ssp_wave_centered = ssp_wave - 1.5 + offset = (_wave[1] - _wave[0]) / 2. + ssp_wave_centered = ssp_wave - offset ssp_flux = np.array(spectrum_collector) grid = SSPGrid(ssp_lg_age_gyr, ssp_lgmet, ssp_wave_centered, ssp_flux) From 249b51a782c230141d9fbaee002558738cbaa79c Mon Sep 17 00:00:00 2001 From: anschaible Date: Thu, 29 May 2025 12:49:31 +0200 Subject: [PATCH 28/76] add particle spectra individually to the in the beginning empty datacube --- ...eline_single_function_shard_map_fits.ipynb | 184 ++++++++++++++---- rubix/config/pipeline_config.yml | 55 ++++++ rubix/core/ifu.py | 68 ++++++- 3 files changed, 271 insertions(+), 36 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb index 0560da96..d8c59572 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb @@ -2,19 +2,28 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "#from jax import config\n", - "#config.update(\"jax_enable_x64\", True)" + "from jax import config\n", + "#config.update(\"jax_enable_x64\", True)\n", + "config.update('jax_num_cpu_devices', 2)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0), CpuDevice(id=1)]\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -36,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -44,9 +53,9 @@ "#import os\n", "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", - "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" + "#os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" ] }, { @@ -97,9 +106,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-29 12:44:22,344 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-05-29 12:44:22,344 - rubix - INFO - Rubix version: 0.0.post400+gee789d5.d20250306\n", + "2025-05-29 12:44:22,344 - rubix - INFO - JAX version: 0.5.0\n", + "2025-05-29 12:44:22,344 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -109,7 +135,7 @@ "galaxy_id = \"g7.66e11\"\n", "\n", "config_NIHAO = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", " \n", " \"logger\": {\n", " \"log_level\": \"DEBUG\",\n", @@ -166,12 +192,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "config_TNG = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", " \n", " \"logger\": {\n", " \"log_level\": \"DEBUG\",\n", @@ -194,8 +220,8 @@ " },\n", " \n", " \"subset\": {\n", - " \"use_subset\": False,\n", - " \"subset_size\": 200000,\n", + " \"use_subset\": True,\n", + " \"subset_size\": 1000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -208,7 +234,7 @@ " \"output_path\": \"output\",\n", "\n", " \"telescope\":\n", - " {\"name\": \"MUSE_WFM\",\n", + " {\"name\": \"MUSE\",\n", " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", " \"lsf\": {\"sigma\": 0.5},\n", " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", @@ -216,13 +242,13 @@ " {\"name\": \"PLANCK15\"},\n", " \n", " \"galaxy\":\n", - " {\"dist_z\": 0.01,\n", + " {\"dist_z\": 0.1,\n", " \"rotation\": {\"type\": \"edge-on\"},\n", " },\n", " \n", " \"ssp\": {\n", " \"template\": {\n", - " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " \"name\": \"FSPS\", #\"Mastar_CB19_SLOG_1_5\"\n", " },\n", " \"dust\": {\n", " \"extinction_model\": \"Cardelli89\",\n", @@ -321,9 +347,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -331,9 +366,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-05-29 12:44:22,756 - rubix - INFO - Getting rubix data...\n", + "2025-05-29 12:44:22,756 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-05-29 12:44:22,804 - rubix - INFO - Centering stars particles\n", + "2025-05-29 12:44:23,331 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", + "2025-05-29 12:44:23,333 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", + "2025-05-29 12:44:23,333 - rubix - INFO - Setting up the pipeline...\n", + "2025-05-29 12:44:23,333 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-05-29 12:44:23,334 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-05-29 12:44:23,334 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-29 12:44:23,342 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-29 12:44:23,476 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-29 12:44:23,483 - rubix - INFO - Getting cosmology...\n", + "2025-05-29 12:44:23,502 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-29 12:44:23,541 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-29 12:44:23,549 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-05-29 12:44:23,713 - rubix - INFO - Assembling the pipeline...\n", + "2025-05-29 12:44:23,713 - rubix - INFO - Compiling the expressions...\n", + "2025-05-29 12:44:23,713 - rubix - INFO - Number of devices: 2\n", + "2025-05-29 12:44:23,840 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-05-29 12:44:23,880 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-05-29 12:44:23,882 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-05-29 12:44:23,889 - rubix - INFO - Calculating IFU cube...\n", + "2025-05-29 12:44:23,890 - rubix - DEBUG - Input shapes: Metallicity: 500, Age: 500\n", + "2025-05-29 12:44:23,981 - rubix - DEBUG - Calculation Finished! Spectra shape: (500, 5994)\n", + "2025-05-29 12:44:23,982 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-05-29 12:44:23,984 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-05-29 12:44:23,984 - rubix - DEBUG - Doppler Shifted SSP Wave: (500, 5994)\n", + "2025-05-29 12:44:23,984 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-05-29 12:44:24,010 - rubix - INFO - Calculating Data Cube...\n", + "2025-05-29 12:44:24,012 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-05-29 12:44:24,012 - rubix - INFO - Convolving with PSF...\n", + "2025-05-29 12:44:24,013 - rubix - INFO - Convolving with LSF...\n", + "2025-05-29 12:44:24,015 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-05-29 12:44:25,506 - rubix - INFO - Pipeline run completed in 2.17 seconds.\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "\n", @@ -343,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -359,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -382,12 +472,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#NBVAL_SKIP\n", - "from rubix.core.fits import store_fits\n", + "#from rubix.core.fits import store_fits\n", "\n", "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_ultraWFM\":\n", "# cutted_datatcube = data.stars.datacube[300:600, :, :]\n", @@ -396,7 +486,7 @@ "# cutted_datatcube = data.stars.datacube[100:200, :, :]\n", "# data.stars.datacube = cutted_datatcube\n", "\n", - "store_fits(config_NIHAO, rubixdata, \"./output/\")" + "#store_fits(config_NIHAO, rubixdata, \"./output/\")" ] }, { @@ -410,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -431,9 +521,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -456,6 +557,8 @@ "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", "plt.plot(wave, spectra_sharded[21,15,:])\n", "plt.plot(wave, spectra_sharded[15,21,:])\n", + "plt.plot(wave, spectra_sharded[13,4,:])\n", + "plt.plot(wave, spectra_sharded[4,13,:])\n", "\n", "plt.show()" ] @@ -469,9 +572,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "import numpy as np\n", @@ -494,7 +608,7 @@ "#fig.colorbar(im0, ax=axes[0])\n", "\n", "# Sharded IFU datacube image\n", - "plt.imshow(img32, origin=\"lower\", cmap=\"inferno\")\n", + "plt.imshow(img32, origin=\"lower\", cmap=\"inferno\", vmin=0, vmax=1e5)\n", "plt.title(\"Sharded IFU Datacube\")\n", "plt.colorbar(label=\"Flux [erg/s/cm^2]\")\n", "\n", @@ -514,7 +628,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "rubix", "language": "python", "name": "python3" }, @@ -528,7 +642,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/rubix/config/pipeline_config.yml b/rubix/config/pipeline_config.yml index 450369c2..fc75b28c 100644 --- a/rubix/config/pipeline_config.yml +++ b/rubix/config/pipeline_config.yml @@ -53,6 +53,61 @@ calc_ifu: args: [] kwargs: {} +calc_ifu_memory: + Transformers: + rotate_galaxy: + name: rotate_galaxy + depends_on: null + args: [] + kwargs: {} + filter_particles: + name: filter_particles + depends_on: rotate_galaxy + args: [] + kwargs: {} + spaxel_assignment: + name: spaxel_assignment + depends_on: filter_particles + args: [] + kwargs: {} + + calculate_spectra: + name: calculate_spectra + depends_on: spaxel_assignment + args: [] + kwargs: {} + + scale_spectrum_by_mass: + name: scale_spectrum_by_mass + depends_on: calculate_spectra + args: [] + kwargs: {} + doppler_shift_and_resampling: + name: doppler_shift_and_resampling + depends_on: scale_spectrum_by_mass + args: [] + kwargs: {} + calculate_datacube: + name: calculate_datacube + depends_on: doppler_shift_and_resampling + args: [] + kwargs: {} + convolve_psf: + name: convolve_psf + depends_on: calculate_datacube + args: [] + kwargs: {} + convolve_lsf: + name: convolve_lsf + depends_on: convolve_psf + args: [] + kwargs: {} + apply_noise: + name: apply_noise + depends_on: convolve_lsf + args: [] + kwargs: {} + calc_dusty_ifu: Transformers: rotate_galaxy: diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 18e83e0b..aa69392c 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -2,6 +2,7 @@ import jax import jax.numpy as jnp +from jax import lax from beartype import beartype as typechecker from jaxtyping import Array, Float, jaxtyped @@ -302,7 +303,7 @@ def doppler_shift_and_resampling(rubixdata: RubixData) -> RubixData: @jaxtyped(typechecker=typechecker) -def get_calculate_datacube(config: dict) -> Callable: +def get_calculate_datacube_old(config: dict) -> Callable: """ The function returns the function that calculates the datacube of the stars. @@ -348,3 +349,68 @@ def calculate_datacube(rubixdata: RubixData) -> RubixData: return rubixdata return calculate_datacube + + +@jaxtyped(typechecker=typechecker) +def get_calculate_datacube(config: dict) -> Callable: + """ + The function returns the function that calculates the datacube of the stars. + + Args: + config (dict): The configuration dictionary + + Returns: + The function that calculates the datacube of the stars. + + Example + ------- + >>> from rubix.core.ifu import get_calculate_datacube + >>> calculate_datacube = get_calculate_datacube(config) + + >>> rubixdata = calculate_datacube(rubixdata) + >>> # Access the datacube of the stars + >>> rubixdata.stars.datacube + """ + logger = get_logger(config.get("logger", None)) + telescope = get_telescope(config) + num_spaxels = int(telescope.sbin) + num_segments = num_spaxels ** 2 + wave_grid = telescope.wave_seq + + # Bind the num_spaxels to the function + # calculate_cube_fn = jax.tree_util.Partial(calculate_cube, num_spaxels=num_spaxels) + # calculate_cube_pmap = jax.pmap(calculate_cube_fn) + + @jaxtyped(typechecker=typechecker) + def calculate_datacube(rubixdata: RubixData) -> RubixData: + logger.info("Calculating Data Cube...") + + # 1. extract arrays + specs = rubixdata.stars.spectra # (n_stars, n_wave) + pix = rubixdata.stars.pixel_assignment # (n_stars,) + nstar = specs.shape[0] + + # initial empty cube: (num_segments, n_wave) + init_cube = jnp.zeros((num_segments, wave_grid.shape[-1])) + + def scan_body(cube, i): + # process the single spectrum + spec_i = specs[i] # shape (n_wave,) + pix_i = pix[i] # scalar in [0..nseg) + # accumulate + cube = cube.at[pix_i].add(spec_i) + return cube, None + + # scan over all particle indices 0..n_particles-1 + cube_flat, _ = lax.scan(scan_body, + init_cube, + jnp.arange(nstar, dtype=jnp.int32)) + + # reshape to (n_spaxels, n_spaxels, n_wave) + cube_3d = cube_flat.reshape(num_spaxels, num_spaxels, -1) + + setattr(rubixdata.stars, "datacube", cube_3d) + logger.debug(f"Datacube shape: {cube_3d.shape}") + return rubixdata + + return calculate_datacube \ No newline at end of file From 349fc214d4dd222f0a4ba4f709a4256df59550d6 Mon Sep 17 00:00:00 2001 From: anschaible Date: Mon, 2 Jun 2025 16:30:11 +0200 Subject: [PATCH 29/76] lax scan over particles and and add them to datacube --- ...ine_single_function_shard_map_memory.ipynb | 624 ++++++++++++++++++ rubix/config/pipeline_config.yml | 23 +- rubix/core/ifu.py | 153 ++++- rubix/core/pipeline.py | 11 +- 4 files changed, 785 insertions(+), 26 deletions(-) create mode 100644 notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb new file mode 100644 index 00000000..d7589d40 --- /dev/null +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -0,0 +1,624 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from jax import config\n", + "#config.update(\"jax_enable_x64\", True)\n", + "\n", + "# if we're running on CPU, need to pre-specify # cores for explicit parallelism\n", + "# used to have to do import os; os.environ[\"XLA_FLAGS\"] = \"--xla_force_host_platform_device_count=8\"\n", + "config.update('jax_num_cpu_devices', 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "#import os\n", + "#import multiprocessing\n", + "\n", + "# Logical cores (includes hyperthreads)\n", + "#print(\"Logical cores:\", os.cpu_count())\n", + "\n", + "\n", + "# Total threads/cores via multiprocessing\n", + "#print(\"multiprocessing.cpu_count():\", multiprocessing.cpu_count())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0), CpuDevice(id=1)]\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import os\n", + "\n", + "# Tell XLA to fake 2 host CPU devices\n", + "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", + "\n", + "# Only make GPU 0 and GPU 1 visible to JAX:\n", + "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", + "\n", + "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", + "\n", + "import jax\n", + "\n", + "# Now JAX will list two CpuDevice entries\n", + "print(jax.devices())\n", + "# → [CpuDevice(id=0), CpuDevice(id=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "#import os\n", + "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", + "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RUBIX pipeline\n", + "\n", + "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", + "\n", + "## How to use the Pipeline\n", + "1) Define a `config`\n", + "2) Setup the `pipeline yaml`\n", + "3) Run the RUBIX pipeline\n", + "4) Do science with the mock-data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Config\n", + "\n", + "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", + "\n", + "For the `config` you can choose the following options:\n", + "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", + "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", + "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", + "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", + "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", + "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", + "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", + "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", + "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", + "- `simulation - name`: currently only IllustrisTNG is supported\n", + "- `simulation - args - path`: where the data is stored and how the file will be named\n", + "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", + "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", + "- `telescope - psf`: define the point spread function that is applied to the mock data\n", + "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", + "- `telescope - noise`: define the noise that is applied to the mock data\n", + "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", + "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", + "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", + "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-06-02 16:27:32,280 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-06-02 16:27:32,281 - rubix - INFO - Rubix version: 0.0.post435+g249b51a.d20250602\n", + "2025-06-02 16:27:32,281 - rubix - INFO - JAX version: 0.5.0\n", + "2025-06-02 16:27:32,281 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "\n", + "galaxy_id = \"g8.13e11\"\n", + "\n", + "config_NIHAO = {\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"NihaoHandler\",\n", + " \"args\": {\n", + " \"particle_type\": [\"stars\"],\n", + " \"save_data_path\": \"data\",\n", + " \"snapshot\": \"1024\",\n", + " },\n", + " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", + " \"subset\": {\"use_subset\": False, \"subset_size\": 200000},\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"NIHAO\",\n", + " \"args\": {\n", + " \"path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024',\n", + " \"halo_path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024.z0.000.AHF_halos',\n", + " \"halo_id\": 0,\n", + " },\n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "config_TNG = {\n", + " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 14,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 2000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-14.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Pipeline yaml\n", + "\n", + "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", + "\n", + "```yaml\n", + "calc_ifu:\n", + " Transformers:\n", + " rotate_galaxy:\n", + " name: rotate_galaxy\n", + " depends_on: null\n", + " args: []\n", + " kwargs:\n", + " type: \"face-on\"\n", + " filter_particles:\n", + " name: filter_particles\n", + " depends_on: rotate_galaxy\n", + " args: []\n", + " kwargs: {}\n", + " spaxel_assignment:\n", + " name: spaxel_assignment\n", + " depends_on: filter_particles\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " reshape_data:\n", + " name: reshape_data\n", + " depends_on: spaxel_assignment\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " calculate_spectra:\n", + " name: calculate_spectra\n", + " depends_on: reshape_data\n", + " args: []\n", + " kwargs: {}\n", + "\n", + " scale_spectrum_by_mass:\n", + " name: scale_spectrum_by_mass\n", + " depends_on: calculate_spectra\n", + " args: []\n", + " kwargs: {}\n", + " doppler_shift_and_resampling:\n", + " name: doppler_shift_and_resampling\n", + " depends_on: scale_spectrum_by_mass\n", + " args: []\n", + " kwargs: {}\n", + " calculate_datacube:\n", + " name: calculate_datacube\n", + " depends_on: doppler_shift_and_resampling\n", + " args: []\n", + " kwargs: {}\n", + " convolve_psf:\n", + " name: convolve_psf\n", + " depends_on: calculate_datacube\n", + " args: []\n", + " kwargs: {}\n", + " convolve_lsf:\n", + " name: convolve_lsf\n", + " depends_on: convolve_psf\n", + " args: []\n", + " kwargs: {}\n", + " apply_noise:\n", + " name: apply_noise\n", + " depends_on: convolve_lsf\n", + " args: []\n", + " kwargs: {}\n", + "```\n", + "\n", + "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the pipeline\n", + "\n", + "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config_TNG)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-06-02 16:27:33,246 - rubix - INFO - Getting rubix data...\n", + "2025-06-02 16:27:33,246 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-02 16:27:33,303 - rubix - INFO - Centering stars particles\n", + "2025-06-02 16:27:33,798 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-06-02 16:27:33,799 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-06-02 16:27:33,799 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-02 16:27:33,799 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-02 16:27:33,800 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-02 16:27:33,800 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 16:27:33,808 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 16:27:33,941 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 16:27:33,948 - rubix - INFO - Getting cosmology...\n", + "2025-06-02 16:27:34,166 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 16:27:34,405 - rubix - DEBUG - SSP Wave: (5333,)\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 16:27:34,413 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 16:27:34,652 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 16:27:35,009 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-02 16:27:35,009 - rubix - INFO - Compiling the expressions...\n", + "2025-06-02 16:27:35,009 - rubix - INFO - Number of devices: 2\n", + "2025-06-02 16:27:35,139 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-02 16:27:35,178 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-02 16:27:35,180 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-02 16:27:35,188 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-06-02 16:27:35,294 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-06-02 16:27:35,295 - rubix - INFO - Convolving with PSF...\n", + "2025-06-02 16:27:35,297 - rubix - INFO - Convolving with LSF...\n", + "2025-06-02 16:27:35,299 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-02 16:27:35,996 - rubix - INFO - Pipeline run completed in 2.20 seconds.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "inputdata = pipe.prepare_data()\n", + "rubixdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#print(rubixdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "#inputdata = pipe.prepare_data()\n", + "#shard_rubixdata = pipe.run_sharded_chunked(inputdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Mock-data\n", + "\n", + "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import jax.numpy as jnp\n", + "\n", + "wave = pipe.telescope.wave_seq\n", + "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", + "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", + "spectra_sharded = rubixdata # Spectra of all stars\n", + "#print(spectra.shape)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title(\"Rubix\")\n", + "plt.xlabel(\"Wavelength [Angstrom]\")\n", + "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "#plt.plot(wave, spectra[12,12,:])\n", + "#plt.plot(wave, spectra[8,12,:])\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.title(\"Rubix Sharded\")\n", + "plt.xlabel(\"Wavelength [Angstrom]\")\n", + "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "plt.plot(wave, spectra_sharded[12,12,:])\n", + "plt.plot(wave, spectra_sharded[8,12,:])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot a spacial image of the data cube" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "# get the spectra of the visible wavelengths from the ifu cube\n", + "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", + "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "#visible_spectra.shape\n", + "\n", + "#image = jnp.sum(visible_spectra, axis=2)\n", + "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", + "\n", + "# Plot side by side\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# Original IFU datacube image\n", + "#im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", + "axes[0].set_title(\"Original IFU Datacube\")\n", + "#fig.colorbar(im0, ax=axes[0])\n", + "\n", + "# Sharded IFU datacube image\n", + "im1 = axes[1].imshow(sharded_image, origin=\"lower\", cmap=\"inferno\")\n", + "axes[1].set_title(\"Sharded IFU Datacube\")\n", + "fig.colorbar(im1, ax=axes[1])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DONE!\n", + "\n", + "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/rubix/config/pipeline_config.yml b/rubix/config/pipeline_config.yml index fc75b28c..8f19bdcc 100644 --- a/rubix/config/pipeline_config.yml +++ b/rubix/config/pipeline_config.yml @@ -70,31 +70,14 @@ calc_ifu_memory: depends_on: filter_particles args: [] kwargs: {} - - calculate_spectra: - name: calculate_spectra + calculate_datacube_particlewise: + name: calculate_datacube_particlewise depends_on: spaxel_assignment args: [] kwargs: {} - - scale_spectrum_by_mass: - name: scale_spectrum_by_mass - depends_on: calculate_spectra - args: [] - kwargs: {} - doppler_shift_and_resampling: - name: doppler_shift_and_resampling - depends_on: scale_spectrum_by_mass - args: [] - kwargs: {} - calculate_datacube: - name: calculate_datacube - depends_on: doppler_shift_and_resampling - args: [] - kwargs: {} convolve_psf: name: convolve_psf - depends_on: calculate_datacube + depends_on: calculate_datacube_particlewise args: [] kwargs: {} convolve_lsf: diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index aa69392c..14bebd76 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -14,6 +14,7 @@ cosmological_doppler_shift, resample_spectrum, velocity_doppler_shift, + _velocity_doppler_shift_single, ) from .data import RubixData @@ -303,7 +304,7 @@ def doppler_shift_and_resampling(rubixdata: RubixData) -> RubixData: @jaxtyped(typechecker=typechecker) -def get_calculate_datacube_old(config: dict) -> Callable: +def get_calculate_datacube(config: dict) -> Callable: """ The function returns the function that calculates the datacube of the stars. @@ -352,7 +353,69 @@ def calculate_datacube(rubixdata: RubixData) -> RubixData: @jaxtyped(typechecker=typechecker) -def get_calculate_datacube(config: dict) -> Callable: +def get_particle_spectrum(config: dict) -> Callable: + """ + Returns a function which, for a *single* star with inputs + (age, metallicity, mass, velocity) + will do: + 1) SSP lookup + 2) scale by mass + 3) Doppler‐shift the SSP wavelengths + 4) resample onto the telescope grid + and return the final 1D spectrum. + """ + # 1) the SSP lookup (metallicity, age) -> spectrum_on_ssp_grid + lookup_ssp = get_lookup_interpolation(config) + + # 2) prepare Doppler + resampling + velocity_direction = rubix_config["ifu"]["doppler"]["velocity_direction"] + z_obs = config["galaxy"]["dist_z"] + + # get telescope grid + telescope = get_telescope(config) + target_wavelength = telescope.wave_seq # shape (n_wave_tel,) + + # get the SSP wavelengths for cosmological redshift + ssp_model = get_ssp(config) + ssp_wave0 = cosmological_doppler_shift( + z=z_obs, + wavelength=ssp_model.wavelength + ) # shape (n_wave_ssp,) + + @jaxtyped(typechecker=typechecker) + def particle_spectrum( + age: Float[Array, ""], + metallicity: Float[Array, ""], + mass: Float[Array, ""], + velocity: Float[Array, ""], + ) -> Float[Array, "n_wave_tel"]: + # --- 1) SSP lookup + spec_ssp = lookup_ssp(metallicity, age) # (n_wave_ssp,) + + # --- 2) mass scale + spec_mass = spec_ssp * mass # (n_wave_ssp,) + + # --- 3) Doppler‐shift the SSP wavelengths + shifted_wave = velocity_doppler_shift( + wavelength=ssp_wave0, + velocity=velocity, + direction=velocity_direction, + ) # (n_wave_ssp,) + + # --- 4) resample onto telescope grid + spec_tel = resample_spectrum( + initial_spectrum=spec_mass, + initial_wavelength=shifted_wave, + target_wavelength=target_wavelength, + ) # (n_wave_tel,) + + return spec_tel + + return particle_spectrum + + +@jaxtyped(typechecker=typechecker) +def get_calculate_datacube_laxscan(config: dict) -> Callable: """ The function returns the function that calculates the datacube of the stars. @@ -413,4 +476,88 @@ def scan_body(cube, i): logger.debug(f"Datacube shape: {cube_3d.shape}") return rubixdata - return calculate_datacube \ No newline at end of file + return calculate_datacube + +@jaxtyped(typechecker=typechecker) +def get_calculate_datacube_particlewise(config: dict) -> Callable: + """ + Returns a function that builds the IFU cube by, for each star: + 1) looking up SSP + 2) scaling by mass + 3) Doppler‐shifting + 4) resampling + 5) accumulating into the shared datacube + """ + logger = get_logger(config.get("logger", None)) + telescope = get_telescope(config) + ns = int(telescope.sbin) + nseg = ns * ns + target_wave = telescope.wave_seq # (n_wave_tel,) + + # prepare SSP lookup + lookup_ssp = get_lookup_interpolation(config) + + # prepare Doppler machinery + velocity_direction = rubix_config["ifu"]["doppler"]["velocity_direction"] + z_obs = config["galaxy"]["dist_z"] + ssp_model = get_ssp(config) + ssp_wave0 = cosmological_doppler_shift( + z=z_obs, + wavelength=ssp_model.wavelength + ) # (n_wave_ssp,) + + @jaxtyped(typechecker=typechecker) + def calculate_datacube_particlewise(rubixdata: RubixData) -> RubixData: + logger.info("Calculating Data Cube (combined per‐particle)…") + + stars = rubixdata.stars + ages = stars.age # (n_stars,) + metallicity = stars.metallicity # (n_stars,) + masses = stars.mass # (n_stars,) + velocities = stars.velocity # (n_stars,) + pix_idx = stars.pixel_assignment # (n_stars,) + nstar = ages.shape[0] + + # init flat cube: (nseg, n_wave_tel) + init_cube = jnp.zeros((nseg, target_wave.shape[-1])) + + def body(cube, i): + age_i = ages[i] # scalar + Z_i = metallicity[i] # scalar + m_i = masses[i] # scalar + v_i = velocities[i] # scalar or vector + pix_i = pix_idx[i].astype(jnp.int32) + + # 1) SSP lookup + spec_ssp = lookup_ssp(Z_i, age_i) # (n_wave_ssp,) + # 2) scale by mass + spec_mass = spec_ssp * m_i # (n_wave_ssp,) + # 3) Doppler‐shift wavelengths + shifted_wave = _velocity_doppler_shift_single( + wavelength=ssp_wave0, + velocity=v_i, + direction=velocity_direction, + ) # (n_wave_ssp,) + # 4) resample onto telescope grid + spec_tel = resample_spectrum( + initial_spectrum=spec_mass, + initial_wavelength=shifted_wave, + target_wavelength=target_wave, + ) # (n_wave_tel,) + + # 5) accumulate + cube = cube.at[pix_i].add(spec_tel) + return cube, None + + cube_flat, _ = lax.scan( + body, + init_cube, + jnp.arange(nstar, dtype=jnp.int32) + ) + + cube_3d = cube_flat.reshape(ns, ns, -1) + setattr(rubixdata.stars, "datacube", cube_3d) + logger.debug(f"Datacube shape: {cube_3d.shape}") + return rubixdata + + return jax.jit(calculate_datacube_particlewise) \ No newline at end of file diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index a2e77c58..468069a3 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -31,10 +31,11 @@ ) from .dust import get_extinction from .ifu import ( - get_calculate_datacube, + #get_calculate_datacube, get_calculate_spectra, get_doppler_shift_and_resampling, get_scale_spectrum_by_mass, + get_calculate_datacube_particlewise, ) from .lsf import get_convolve_lsf from .noise import get_apply_noise @@ -106,7 +107,10 @@ def _get_pipeline_functions(self) -> list: self.user_config ) apply_extinction = get_extinction(self.user_config) - calculate_datacube = get_calculate_datacube(self.user_config) + #calculate_datacube = get_calculate_datacube(self.user_config) + calculate_datacube_particlewise = get_calculate_datacube_particlewise( + self.user_config + ) convolve_psf = get_convolve_psf(self.user_config) convolve_lsf = get_convolve_lsf(self.user_config) apply_noise = get_apply_noise(self.user_config) @@ -120,7 +124,8 @@ def _get_pipeline_functions(self) -> list: scale_spectrum_by_mass, doppler_shift_and_resampling, apply_extinction, - calculate_datacube, + #calculate_datacube, + calculate_datacube_particlewise, convolve_psf, convolve_lsf, apply_noise, From 49a6496ac94ef8141a2af3ac2fd2a308129bd3b2 Mon Sep 17 00:00:00 2001 From: anschaible Date: Mon, 2 Jun 2025 17:19:18 +0200 Subject: [PATCH 30/76] lax.scan works and produces same results as old shard map, bit slower, when directly adding to the cube, but hopefully more memory efficient, will be tested, as soon as jarvis is back online --- ...ine_single_function_shard_map_memory.ipynb | 194 ++++++++++++------ rubix/core/ifu.py | 11 +- rubix/core/pipeline.py | 6 +- 3 files changed, 138 insertions(+), 73 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index d7589d40..5e0ad62c 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -7,7 +7,7 @@ "outputs": [], "source": [ "from jax import config\n", - "#config.update(\"jax_enable_x64\", True)\n", + "config.update(\"jax_enable_x64\", True)\n", "\n", "# if we're running on CPU, need to pre-specify # cores for explicit parallelism\n", "# used to have to do import os; os.environ[\"XLA_FLAGS\"] = \"--xla_force_host_platform_device_count=8\"\n", @@ -18,24 +18,6 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "#import os\n", - "#import multiprocessing\n", - "\n", - "# Logical cores (includes hyperthreads)\n", - "#print(\"Logical cores:\", os.cpu_count())\n", - "\n", - "\n", - "# Total threads/cores via multiprocessing\n", - "#print(\"multiprocessing.cpu_count():\", multiprocessing.cpu_count())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -66,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -127,23 +109,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-06-02 16:27:32,280 - rubix - INFO - \n", + "2025-06-02 17:17:57,063 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-06-02 16:27:32,281 - rubix - INFO - Rubix version: 0.0.post435+g249b51a.d20250602\n", - "2025-06-02 16:27:32,281 - rubix - INFO - JAX version: 0.5.0\n", - "2025-06-02 16:27:32,281 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" + "2025-06-02 17:17:57,063 - rubix - INFO - Rubix version: 0.0.post435+g249b51a.d20250602\n", + "2025-06-02 17:17:57,064 - rubix - INFO - JAX version: 0.5.0\n", + "2025-06-02 17:17:57,064 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" ] } ], @@ -171,7 +153,7 @@ " \"snapshot\": \"1024\",\n", " },\n", " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": False, \"subset_size\": 200000},\n", + " \"subset\": {\"use_subset\": False, \"subset_size\": 500000},\n", " },\n", " \"simulation\": {\n", " \"name\": \"NIHAO\",\n", @@ -213,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +251,7 @@ " \n", " \"ssp\": {\n", " \"template\": {\n", - " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " \"name\": \"FSPS\", #\"Mastar_CB19_SLOG_1_5\"\n", " },\n", " \"dust\": {\n", " \"extinction_model\": \"Cardelli89\",\n", @@ -368,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -387,55 +369,57 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-06-02 16:27:33,246 - rubix - INFO - Getting rubix data...\n", - "2025-06-02 16:27:33,246 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-02 16:27:33,303 - rubix - INFO - Centering stars particles\n", - "2025-06-02 16:27:33,798 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-06-02 16:27:33,799 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-06-02 16:27:33,799 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-02 16:27:33,799 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-02 16:27:33,800 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-02 16:27:33,800 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-02 17:17:57,387 - rubix - INFO - Getting rubix data...\n", + "2025-06-02 17:17:57,388 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-02 17:17:57,420 - rubix - INFO - Centering stars particles\n", + "2025-06-02 17:17:57,939 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-06-02 17:17:57,940 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-06-02 17:17:57,941 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-02 17:17:57,941 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-02 17:17:57,941 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-02 17:17:57,942 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 16:27:33,808 - rubix - INFO - Getting cosmology...\n", + "2025-06-02 17:17:57,950 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 16:27:33,941 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-02 17:17:58,088 - rubix - INFO - Calculating spatial bin edges...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 16:27:33,948 - rubix - INFO - Getting cosmology...\n", - "2025-06-02 16:27:34,166 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-02 17:17:58,095 - rubix - INFO - Getting cosmology...\n", + "2025-06-02 17:17:58,107 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 16:27:34,405 - rubix - DEBUG - SSP Wave: (5333,)\n", + "2025-06-02 17:17:58,143 - rubix - DEBUG - SSP Wave: (5994,)\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 16:27:34,413 - rubix - INFO - Getting cosmology...\n", + "2025-06-02 17:17:58,151 - rubix - INFO - Getting cosmology...\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 16:27:34,652 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 16:27:35,009 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-02 16:27:35,009 - rubix - INFO - Compiling the expressions...\n", - "2025-06-02 16:27:35,009 - rubix - INFO - Number of devices: 2\n", - "2025-06-02 16:27:35,139 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-02 16:27:35,178 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-02 16:27:35,180 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-02 16:27:35,188 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-06-02 16:27:35,294 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-06-02 16:27:35,295 - rubix - INFO - Convolving with PSF...\n", - "2025-06-02 16:27:35,297 - rubix - INFO - Convolving with LSF...\n", - "2025-06-02 16:27:35,299 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-02 16:27:35,996 - rubix - INFO - Pipeline run completed in 2.20 seconds.\n" + "2025-06-02 17:17:58,188 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:17:58,386 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-02 17:17:58,386 - rubix - INFO - Compiling the expressions...\n", + "2025-06-02 17:17:58,387 - rubix - INFO - Number of devices: 2\n", + "2025-06-02 17:17:58,577 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-02 17:17:58,631 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-02 17:17:58,641 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-02 17:17:58,655 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-06-02 17:17:58,843 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-06-02 17:17:58,844 - rubix - INFO - Convolving with PSF...\n", + "2025-06-02 17:17:58,847 - rubix - INFO - Convolving with LSF...\n", + "2025-06-02 17:17:58,852 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-02 17:18:00,462 - rubix - INFO - Pipeline run completed in 2.52 seconds.\n" ] } ], @@ -446,6 +430,80 @@ "rubixdata = pipe.run_sharded(inputdata)" ] }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:18:00,583 - rubix - INFO - Getting rubix data...\n", + "2025-06-02 17:18:00,584 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-02 17:18:00,600 - rubix - INFO - Centering stars particles\n", + "2025-06-02 17:18:00,916 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-06-02 17:18:00,919 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-06-02 17:18:00,923 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-02 17:18:00,925 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-02 17:18:00,927 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-02 17:18:00,930 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:18:00,944 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:18:00,961 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:18:00,971 - rubix - INFO - Getting cosmology...\n", + "2025-06-02 17:18:00,992 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:18:01,042 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:18:01,066 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:18:01,126 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-02 17:18:01,204 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-02 17:18:01,208 - rubix - INFO - Compiling the expressions...\n", + "2025-06-02 17:18:01,220 - rubix - INFO - Number of devices: 2\n", + "2025-06-02 17:18:01,337 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-02 17:18:01,370 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-02 17:18:01,372 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-02 17:18:01,373 - rubix - INFO - Calculating IFU cube...\n", + "2025-06-02 17:18:01,374 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", + "2025-06-02 17:18:01,424 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", + "2025-06-02 17:18:01,425 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-06-02 17:18:01,427 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-06-02 17:18:01,427 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", + "2025-06-02 17:18:01,427 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-06-02 17:18:01,443 - rubix - INFO - Calculating Data Cube...\n", + "2025-06-02 17:18:01,444 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-06-02 17:18:01,445 - rubix - INFO - Convolving with PSF...\n", + "2025-06-02 17:18:01,446 - rubix - INFO - Convolving with LSF...\n", + "2025-06-02 17:18:01,447 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-02 17:18:02,866 - rubix - INFO - Pipeline run completed in 1.94 seconds.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "config_TNG[\"pipeline\"][\"name\"] = \"calc_ifu\"\n", + "pipe = RubixPipeline(config_TNG)\n", + "\n", + "inputdata = pipe.prepare_data()\n", + "rubixdata_old = pipe.run_sharded(inputdata)" + ] + }, { "cell_type": "code", "execution_count": 9, @@ -504,7 +562,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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" ] @@ -517,7 +575,7 @@ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", "\n", - "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", + "spectra = rubixdata_old#.stars.datacube # Spectra of all stars\n", "spectra_sharded = rubixdata # Spectra of all stars\n", "#print(spectra.shape)\n", "\n", @@ -526,8 +584,8 @@ "plt.title(\"Rubix\")\n", "plt.xlabel(\"Wavelength [Angstrom]\")\n", "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "#plt.plot(wave, spectra[12,12,:])\n", - "#plt.plot(wave, spectra[8,12,:])\n", + "plt.plot(wave, spectra[12,12,:])\n", + "plt.plot(wave, spectra[8,12,:])\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.title(\"Rubix Sharded\")\n", @@ -553,9 +611,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -566,20 +624,20 @@ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", - "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "visible_spectra = rubixdata_old[ :, :, visible_indices[0]]\n", "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", "#visible_spectra.shape\n", "\n", - "#image = jnp.sum(visible_spectra, axis=2)\n", + "image = jnp.sum(visible_spectra, axis=2)\n", "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", "\n", "# Plot side by side\n", "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", "\n", "# Original IFU datacube image\n", - "#im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", + "im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", "axes[0].set_title(\"Original IFU Datacube\")\n", - "#fig.colorbar(im0, ax=axes[0])\n", + "fig.colorbar(im0, ax=axes[0])\n", "\n", "# Sharded IFU datacube image\n", "im1 = axes[1].imshow(sharded_image, origin=\"lower\", cmap=\"inferno\")\n", diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 14bebd76..66cff3c0 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -417,8 +417,14 @@ def particle_spectrum( @jaxtyped(typechecker=typechecker) def get_calculate_datacube_laxscan(config: dict) -> Callable: """ - The function returns the function that calculates the datacube of the stars. + The function returns the function that calculates the datacube of the stars. + It takes RubixData as input. It calculates the spectrum for one stellar particle, + weights it by mass, doppler shifts it, resamples it to the telescope wavelength grid, + and finally adds the spectrum at the right position in the datacube. + This is done for every stellar particle in the RubixData object. + This is done by using a JAX lax.scan, which is a more efficient way to do this than a for loop. + Args: config (dict): The configuration dictionary @@ -560,4 +566,5 @@ def body(cube, i): logger.debug(f"Datacube shape: {cube_3d.shape}") return rubixdata - return jax.jit(calculate_datacube_particlewise) \ No newline at end of file + #return jax.jit(calculate_datacube_particlewise) + return calculate_datacube_particlewise \ No newline at end of file diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 468069a3..24dd27ad 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -31,7 +31,7 @@ ) from .dust import get_extinction from .ifu import ( - #get_calculate_datacube, + get_calculate_datacube, get_calculate_spectra, get_doppler_shift_and_resampling, get_scale_spectrum_by_mass, @@ -107,7 +107,7 @@ def _get_pipeline_functions(self) -> list: self.user_config ) apply_extinction = get_extinction(self.user_config) - #calculate_datacube = get_calculate_datacube(self.user_config) + calculate_datacube = get_calculate_datacube(self.user_config) calculate_datacube_particlewise = get_calculate_datacube_particlewise( self.user_config ) @@ -124,7 +124,7 @@ def _get_pipeline_functions(self) -> list: scale_spectrum_by_mass, doppler_shift_and_resampling, apply_extinction, - #calculate_datacube, + calculate_datacube, calculate_datacube_particlewise, convolve_psf, convolve_lsf, From d14bd2be963fd028dbb680963c8255d273a2efcd Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 3 Jun 2025 09:49:25 +0200 Subject: [PATCH 31/76] notebook for comparison between old and new method in shard_map --- ...ine_single_function_shard_map_memory.ipynb | 198 ++++++++---------- 1 file changed, 93 insertions(+), 105 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index 5e0ad62c..93ea83b0 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -11,7 +11,7 @@ "\n", "# if we're running on CPU, need to pre-specify # cores for explicit parallelism\n", "# used to have to do import os; os.environ[\"XLA_FLAGS\"] = \"--xla_force_host_platform_device_count=8\"\n", - "config.update('jax_num_cpu_devices', 2)" + "config.update('jax_num_cpu_devices', 16)" ] }, { @@ -23,7 +23,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[CpuDevice(id=0), CpuDevice(id=1)]\n" + "[CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15)]\n" ] } ], @@ -56,9 +56,9 @@ "#import os\n", "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" + "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" ] }, { @@ -109,23 +109,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-06-02 17:17:57,063 - rubix - INFO - \n", + "2025-06-03 09:48:00,970 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-06-02 17:17:57,063 - rubix - INFO - Rubix version: 0.0.post435+g249b51a.d20250602\n", - "2025-06-02 17:17:57,064 - rubix - INFO - JAX version: 0.5.0\n", - "2025-06-02 17:17:57,064 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" + "2025-06-03 09:48:00,972 - rubix - INFO - Rubix version: 0.0.post437+g49a6496.d20250603\n", + "2025-06-03 09:48:00,972 - rubix - INFO - JAX version: 0.6.0\n", + "2025-06-03 09:48:00,972 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15)] devices\n" ] } ], @@ -138,7 +138,7 @@ "galaxy_id = \"g8.13e11\"\n", "\n", "config_NIHAO = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", + " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", " \n", " \"logger\": {\n", " \"log_level\": \"DEBUG\",\n", @@ -153,7 +153,7 @@ " \"snapshot\": \"1024\",\n", " },\n", " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": False, \"subset_size\": 500000},\n", + " \"subset\": {\"use_subset\": False, \"subset_size\": 2000},\n", " },\n", " \"simulation\": {\n", " \"name\": \"NIHAO\",\n", @@ -357,14 +357,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n" ] } ], "source": [ "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config_TNG)" + "pipe = RubixPipeline(config_NIHAO)" ] }, { @@ -376,50 +376,44 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-06-02 17:17:57,387 - rubix - INFO - Getting rubix data...\n", - "2025-06-02 17:17:57,388 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-02 17:17:57,420 - rubix - INFO - Centering stars particles\n", - "2025-06-02 17:17:57,939 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-06-02 17:17:57,940 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-06-02 17:17:57,941 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-02 17:17:57,941 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-02 17:17:57,941 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-02 17:17:57,942 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-02 17:17:57,950 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-02 17:17:58,088 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-02 17:17:58,095 - rubix - INFO - Getting cosmology...\n", - "2025-06-02 17:17:58,107 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:02,133 - rubix - INFO - Getting rubix data...\n", + "2025-06-03 09:48:02,134 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-03 09:48:02,193 - rubix - INFO - Centering stars particles\n", + "2025-06-03 09:48:03,031 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-06-03 09:48:03,034 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-06-03 09:48:03,035 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-03 09:48:03,036 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-03 09:48:03,037 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-03 09:48:03,039 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:17:58,143 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:03,063 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 09:48:03,237 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 09:48:03,246 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 09:48:03,706 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:17:58,151 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:04,191 - rubix - DEBUG - SSP Wave: (5333,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:04,202 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:17:58,188 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:04,685 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:17:58,386 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-02 17:17:58,386 - rubix - INFO - Compiling the expressions...\n", - "2025-06-02 17:17:58,387 - rubix - INFO - Number of devices: 2\n", - "2025-06-02 17:17:58,577 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-02 17:17:58,631 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-02 17:17:58,641 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-02 17:17:58,655 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-06-02 17:17:58,843 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-06-02 17:17:58,844 - rubix - INFO - Convolving with PSF...\n", - "2025-06-02 17:17:58,847 - rubix - INFO - Convolving with LSF...\n", - "2025-06-02 17:17:58,852 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-02 17:18:00,462 - rubix - INFO - Pipeline run completed in 2.52 seconds.\n" + "2025-06-03 09:48:05,306 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-03 09:48:05,307 - rubix - INFO - Compiling the expressions...\n", + "2025-06-03 09:48:05,308 - rubix - INFO - Number of devices: 16\n", + "2025-06-03 09:48:05,457 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-03 09:48:05,569 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-03 09:48:05,574 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-03 09:48:05,601 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-06-03 09:48:05,871 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-06-03 09:48:05,872 - rubix - INFO - Convolving with PSF...\n", + "2025-06-03 09:48:05,875 - rubix - INFO - Convolving with LSF...\n", + "2025-06-03 09:48:05,880 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-03 09:48:08,231 - rubix - INFO - Pipeline run completed in 5.20 seconds.\n" ] } ], @@ -439,66 +433,60 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-02 17:18:00,583 - rubix - INFO - Getting rubix data...\n", - "2025-06-02 17:18:00,584 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-02 17:18:00,600 - rubix - INFO - Centering stars particles\n", - "2025-06-02 17:18:00,916 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-06-02 17:18:00,919 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-06-02 17:18:00,923 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-02 17:18:00,925 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-02 17:18:00,927 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-02 17:18:00,930 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-02 17:18:00,944 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-02 17:18:00,961 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:18:00,971 - rubix - INFO - Getting cosmology...\n", - "2025-06-02 17:18:00,992 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:08,865 - rubix - INFO - Getting rubix data...\n", + "2025-06-03 09:48:08,866 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-03 09:48:08,893 - rubix - INFO - Centering stars particles\n", + "2025-06-03 09:48:09,396 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-06-03 09:48:09,397 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-06-03 09:48:09,398 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-03 09:48:09,398 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-03 09:48:09,399 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-03 09:48:09,400 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:18:01,042 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:09,410 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 09:48:09,419 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 09:48:09,428 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 09:48:09,898 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:18:01,066 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:10,376 - rubix - DEBUG - SSP Wave: (5333,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:10,389 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:18:01,126 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + "2025-06-03 09:48:10,877 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-02 17:18:01,204 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-02 17:18:01,208 - rubix - INFO - Compiling the expressions...\n", - "2025-06-02 17:18:01,220 - rubix - INFO - Number of devices: 2\n", - "2025-06-02 17:18:01,337 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-02 17:18:01,370 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-02 17:18:01,372 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-02 17:18:01,373 - rubix - INFO - Calculating IFU cube...\n", - "2025-06-02 17:18:01,374 - rubix - DEBUG - Input shapes: Metallicity: 1000, Age: 1000\n", - "2025-06-02 17:18:01,424 - rubix - DEBUG - Calculation Finished! Spectra shape: (1000, 5994)\n", - "2025-06-02 17:18:01,425 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-06-02 17:18:01,427 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-06-02 17:18:01,427 - rubix - DEBUG - Doppler Shifted SSP Wave: (1000, 5994)\n", - "2025-06-02 17:18:01,427 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-06-02 17:18:01,443 - rubix - INFO - Calculating Data Cube...\n", - "2025-06-02 17:18:01,444 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-06-02 17:18:01,445 - rubix - INFO - Convolving with PSF...\n", - "2025-06-02 17:18:01,446 - rubix - INFO - Convolving with LSF...\n", - "2025-06-02 17:18:01,447 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-02 17:18:02,866 - rubix - INFO - Pipeline run completed in 1.94 seconds.\n" + "2025-06-03 09:48:11,356 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-03 09:48:11,357 - rubix - INFO - Compiling the expressions...\n", + "2025-06-03 09:48:11,358 - rubix - INFO - Number of devices: 16\n", + "2025-06-03 09:48:11,454 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-03 09:48:11,542 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-03 09:48:11,546 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-03 09:48:11,548 - rubix - INFO - Calculating IFU cube...\n", + "2025-06-03 09:48:11,549 - rubix - DEBUG - Input shapes: Metallicity: 125, Age: 125\n", + "2025-06-03 09:48:11,678 - rubix - DEBUG - Calculation Finished! Spectra shape: (125, 5333)\n", + "2025-06-03 09:48:11,679 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-06-03 09:48:11,683 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-06-03 09:48:11,684 - rubix - DEBUG - Doppler Shifted SSP Wave: (125, 5333)\n", + "2025-06-03 09:48:11,684 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-06-03 09:48:11,719 - rubix - INFO - Calculating Data Cube...\n", + "2025-06-03 09:48:11,723 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-06-03 09:48:11,724 - rubix - INFO - Convolving with PSF...\n", + "2025-06-03 09:48:11,727 - rubix - INFO - Convolving with LSF...\n", + "2025-06-03 09:48:11,730 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-03 09:48:14,080 - rubix - INFO - Pipeline run completed in 4.68 seconds.\n" ] } ], "source": [ "#NBVAL_SKIP\n", - "config_TNG[\"pipeline\"][\"name\"] = \"calc_ifu\"\n", - "pipe = RubixPipeline(config_TNG)\n", + "config_NIHAO[\"pipeline\"][\"name\"] = \"calc_ifu\"\n", + "pipe = RubixPipeline(config_NIHAO)\n", "\n", "inputdata = pipe.prepare_data()\n", "rubixdata_old = pipe.run_sharded(inputdata)" @@ -562,7 +550,7 @@ "outputs": [ { "data": { - "image/png": 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z2OW2UAaiCasO0q6Tl+l/c3e6DRyBJkncAOK5Ccr6JgVGeyIJd5AatmC3/fMZm47Tyn1nw3pMABA8o//dS1uOXrR/zoUFDlUOIShvevyn1XTw7FXq9v0KOnlROx4Fis9rH/69zf75jRQkSZGES0FXSLFo6OwddOXGrbAek5WFNUl6++23KSYmxuGjYsWK9q9fv36d+vXrR3nz5qUcOXLQPffcQydOGFu7C8a6lpLqcps8k+SupMEdHm3juurpG4/q/p7rPgYFf4JIr7GraOr6jGPC/k+RZeam43T1ZipVLZJI99ctKm77c92RcB8WmADiUnS6pHGhKSdJvp7DF+88RfePXEZ7T132KzbFxRq/RxN3x+OL6pX7zzrcBpFj0hpl5uixpqWoeJ5sojKG32tg0ZmkKlWq0LFjx+wfS5YssX/t+eefp2nTptHEiRNp0aJFdPToUeratWtYjxc8u3bT9YQsT9T4Olr30cztoq76mfHyrI1n7tY9uWNEuR3qhiPLkt2nxf/bVS1ETdI7V+0KwQJtiAyIS9HHucU3N2qQB/Dkf+vx6A8rRTLSP71jqq9i5U2aDHL1hmv8RbldZL1H+XqH3V2zCDUolUf8e+cJ/Yk4RFkL8NjYWCpYsKDL7RcuXKDRo0fT+PHjqWXLluK2MWPGUKVKlWj58uXUsGHDMBwt6FkM7+yWlLSk+piQaHWhO3/1Jm09epEals7rsM+F/WdIP4LXI3lz00MQ4f0rflp2gDpUK0Ql82V3ez9fZ8ggfPjiaPkeZRPhpmXzUeb099De01fCfGRgFohL0SflluM5+lqKY1Mhf8/hctMXnlXiaooqhZO8fl/rzxZToA2J5mw9Tr0al6KsWTKL265qxF/MJEWOjYfP0+UbtyhXtjiqXDiRyhTIIW7fexpJkmVnknbt2kWFCxem0qVLU7du3ejgwYPi9jVr1lBKSgq1bt3afl8ueShevDgtW+Z+k7UbN27QxYsXHT6s6tyVm+JEGkpaSY08seNrVZrWYFvNd+fSw9+voGluSvDk1pl6nr+nIMIbznL5QsevM0aSxy3b73I/X0chIXw2HL4gSm94r6yqRZLsyS+XNRjVGh4im9FxiSE2Zdh/+orbNaLB4pxAlMmf3aGywd/1supD8DrHlv9bRB2+WiIG14Kt7ReL6ZNZO8Rm7KpeY5S9nWQ3nZJDMC/uSMialFEG70rmVWLT/jPoUGjJJKlBgwY0duxYmjVrFo0YMYL27dtHzZo1o0uXLtHx48cpS5YslCtXLofvSU5OFl9zZ8iQIZSUlGT/KFasGFkVtzbmmZRQtgC96BQccsTHOgSi96Zvpd0nXcua3BUeeMo9pm88Zkjp2+lLN+nqTe2FkcvSZxzkevYv5+92Pc70n7vj+CXsb2By3FmKNS6TVwQifo/myxEvbjuIYGR5wYhLDLFJwXvK3fbpQuoyfGlIf+41p3M8n6/lxIi7smpVQnij7iyhnlfYxevGLLTnxMtdPFNvX773jH2wT6ssiys5eOH/gTOYKTc79T2kloCXzJdN/B+/O4smSe3ataP77ruPqlevTm3btqUZM2bQ+fPn6ffff/f7MQcNGiRKItSPQ4es2z7xUvqJOpRdu5zzA55hcR6h86XMwFO6UVUqafh15UH7iJqv654eGb2Car83VzO5SdGY+jp9WWME1Eb07rQtYnSP90waNHkjzdikncRBeKmLYJuWUwIRK5lXCUb7EYwsLxhxiSE2Kdam7wt0+Fxo9ybjBhzOa1HlEjueYf5cmpXR/bjp/8+fI8F+W2p63OA1Jm//tYXmbfWvsUf9D+bTs17WPKnbT1zRWI+kNqS4/dOF1GLoQpq95Tg9/9t6rL80IZ59XHvwvPh3s/TYxI0b2PmrKWKZAViw3E7Go3Ply5en3bt3i3rwmzdviuAk4y5CWrXiqvj4eEpMTHT4sLpQLpdxLjvj0S53I2F6Zlyc7yN/LidDgyZvoi/m7aI/1x3WPZOknoDUrkNnrtz0WIq3ZNdpjyUZPy47IP7Px/HrykP09C9rdR0HhHYUe90h5ZzSvFx+++0l0ssaMGIHwYhLDLFJ4W5T72BzbpTAa5Scz+ejFu11iTMxOpMvOfalpNeYz992ksb+t58e+2m1S+MIvf72Mth27MJ12nzkgmZnWcaJoLq5+5Pj1ogunveP8lwaCqE3a/Nxce1SKl92KpZ+bZItSywlJypVDgdQ5RAWpkqSLl++THv27KFChQpRnTp1KC4ujubPn2//+o4dO0RteKNGjcJ6nJFG7fbGDQqOX3DcZTwUM0laMzvrD52nuu/Py/g+d4/n9Lkc076av0vMHsmB7vnfNojRMj2ypS92VfGaFIeflWajE9JeFjzj9Ouqg26OEyV2ZsbBhzsvfv3PLvFvbtigBiLHmSQEInCEuGQstVGKnIzwuTfY63icm/xw0uIcm7ib2A9L9lEdHbFJpT6q/Fit/rfIpW1zhTdmkpGDPXLjiLu+XkJH3MzMcaMaZ+euYu2lWXCJJ8emr/7ZJT5/oJ5jGa46gIcqBwsmSS+++KJoobp//37677//qEuXLpQ5c2Z66KGHRM12nz59aODAgbRgwQKxYLZXr14iEKGDkOLC1RQat/yAOGFyCYO7mRn1HNn/17XUcMh8Wpde7hCKmSTOX7RmX3htktbMjero+Wv026qDtHCHY6BxniXiWRvnrkRyYuNLksR758i2HnNdWD1tg3azCOyTZF78d8F7bVUaPIt+X61sdDzwjvIO9ymRL7JmkniNwcDf19OQGRmbRoIxEJcCx+Vcv686JGLTHqd9hOSZJD5180Viy/8p5WD+zrb4M4MlkiSnHxebOYbenb7VZcBMtubAWZq4OqNUUn1Y5zjHLcLjMjs+V39xxzPn+OnM3X5NKei8alo8aN3gw/kiNh06e43y5chCjzYq4XAfdQAvUmaSdhy/RD1+WBk1yw3C2gL88OHDIvCcOXOG8ufPT02bNhVtVPnf7PPPP6dMmTKJzfq4MxDXhw8fPjych2wqz05YR4uk0arPH6hBXWopG2M6L/584qfVNCe9LnrCykNUq3juoByTc57GSc3Lf2x0uZ+3BbK8P9JfGgmJVhc5ucW4LxLiHJOk7PGOn2vlnDwKyoHPea2S+tqC+fAO96v2ZwwMtKmcTLWd3v+lIqyL0KdzdtDktcrmtz0al6TCubKG+5CiBuJS4Np87rjudMPgOygpW5xL2dt/e06Lcz2vuWDcXVJtomI057K5P9YedjmPq416PHnsx9VOMzFquR3pWtPqb6dabjDjrkGSp/3+3pyy2ZBjAOPN3XbCoaPqc63KiRK7SJ1JupWaRn1+XCXWG3Lb8vbVClGkC2uSNGHCBI9fT0hIoGHDhokPcCUnSIwvmrSSJJ5tkWntLWQUrbIztYGEbMtRz+1vtRIkd0mSv4EoPjaT24W9XMLQU6OdauZMmbAnUoRuHMsXX4M7VqZWFQu43Kd4+mgdtyXmWZrs0gWJ2ew8cYl+/C+jDT2P1CNJMg7ikvGOXrhmT5Lkcrvuo1eGbFNu56VQWnFDz493V6qmdez+DuBxIinHGXlQ8Z/tJ+wbjurd7w/MaWl6y+/m5fNTj0YlqKVGbFLbgEfCTNLPyw/YG7LwzBj/Tch/75HIVGuSwNjuPe6kN8MxFM9WDZm5jfaeCu5oh1Yg8jewZnFKktQgw7XxTT76R7MckEsqwtnhmxd38rEt3HEyfAcRYf7brYwOd29YgjrVKKyZAPGeSXmyZ4mIYMSlPvJbPtjrDAECJYcmT4N0wUiSuBR96OztQU8itAbwjIpNN9KPnQdIeo9drfk97rrbhcqHM7aJLUf4WgC842uJZent2we0LketKiVrXsOVsJfbmX8macKqQ947AUcYJElRJCaM3YVenLhRdAZ6/2//1khwMNGz8atWzNFamKpHlljH8jq1Hp5r493xdWTQ3w0K3Xnlj41iluutv7YY+rjRvB5p5X5l1LVh6Twe7xsJwYifz9z00s6c6ckekiSIJJljQpskdR3+Hw1bsIdmbtbX0MfZH2sOi/W/TGvdr31NkoFVDs5JEscmTj7ucCpjlIViA1tPvl28V8RwHsgD73aevCRK7XhtdPUiGduZuItLpy8Hv7lJIA6dvUrbj18SM0dZ05cycOfFSIckyYKCkSStN6AZBI9CeUs4tJIOf4NfvEYgOnbhmsdFu3l9rJd3VyfuL7V+2V0nI8jAFzS7Tl4WJXRZMmeiGsUcNwB1V9aw97R5kyS+COF1U/x87qurdEE6fjHyAxFYh6dOoMEst/MXN/B5evwar8en9TU9a5z0xCaeBft5uXZnVU9l7eFg1DqsaMYDu+p7g9fHxnoo78mZECcaOrD9p81b5TAnffCuXsncVKFgzqgZwEOSFOWtVUOVJDmPfPnrlxUHaKub9Uo/LdtPqw+4JmP+zqo4HzN3EGo05B+P36MmafIiWk+C1a0J66K8e+i75faR12pFk1wadTgrl5zDbVdDswWiJmXzUpkC2aMmEEF0kyfgPZ26zHpeW7r7jGgNzqP5zngQhjux8gw/aTSH8FXFgjldYtP87SfFlheeaDVzCAd/Kzus4sTF61Tvg3n0zjSlQ2Hdkt6baJUroCQdW49dILOak771yh2VC1KhJGVj5eMXIn8wF0lSFJG7BnlaNxOMdXRGrdN5/c/N1P6rfzW/9va0rfT4T9r12Jr371hZ8/Z3765CU/s1EaPxMj0Xm2or1oQ4fX86gewqv//0FdqQvvGp1vHh4ti9M5dvOCxuvqu69y471YsoM02bDps3EC1Kb4nfunJyRiDCTBKYnFyK5mkAz4wzSSpuDf7kOO3488ofm+iTWTt0P9br7Su53FY+OQcNvqsy/dS7vktsmrTGe7KlziTdWcXzpsZGlIKv2n9WbNOh4s1sVaOX7EOi5MHyvWfszT/4mq2tjt9X9aJKOd5Gk8amKzdu2QewuXtscqIam7AmCQzAF7u839HVm4FNl/MeDypPp79gdBsxY2grkP6H6qxbgxKi9CreKdHRMxL3b3o3Gp6VaF0p2ev9306f5eISPv4de2t9Lrvt04V097CldPicMsXOe1zJHv5uue7HshqujVaT2WnPNKWejUt6/Z5q6XXh3DL8/FX3JZfhwheX29JnueqWyJMRiJAsQ5Bw0wN3e8P5Qs6LPMUKMydJbINBF6ncKEarTK1301IibsU7rZfli1C9XTxzpzeg8UbtMMsJji972vD97xu5jBp/9I+9uyZvZqs6eekG/bjsgO7Hsxq1UoE3Lp43sAVVKpTo9XuqF00fwJOSUTPZceKS+NstkDNebNJeEDNJ4OvFDS9qczeC1mX4UrGXAXeHYfxmc+4KwicmbxdD3J5apbWIVDVq8V56Z9oWS82saSWJ8U6jdfJ+Bd7wwkQ9VYt8Qvxz3WF65PsV4nf8/t+umwDy79V5VE9+r/DGgU/9rNTEy8y8dibc1GTitvIFRKmdns6P3KJY3bjPjMGIy3ku3bgl9ukqnT87FUpS2n5zF8Ybt8Lb2QoiE79vuPzHU9OD/r+us88U8Ey6fMHO5y3e68jbAF+qzpkkrhSYsk7Z/ytauRuj3Cedz911t9NDXTTvDe+7M3ntYZHgPP3LWpf1U/y71brmWC9VNwxbsJta/c91LfFajbJ4UGw/pgzgdaxRmEqmb2KudyaJ45oZz/U70gcl1bVI0VTlgCQpRBs/NvtkAQ1fuEfz62oHkAXblVIa3p+n7vvzaF16MwSe2uYTGe9ibES5HRuzdL9hXby444qnZgfhEudlnZRzIPIlSeKZJD3zcVwC8fxvG+yjR7M2n3Cp4+UZolcnb3Tb8GH2lhNum1OcjIKTUDBsSw9EFQspJ229qqWP2JmxrEENrmXy56C4zJkod7Y4+3v4ZBSUNUDodfhqCTX4cD7tOqG8t9zhC3heX1n1rdlU5a3Z9nIq7mb68HcrxMWy/nI7zwMBA35bT0bZe8p7x9RQ01PJEcgaX72l4Jz8Dvx9g/1zblok45jEsUlda6LKmZCxHnfobO0Sw783HfOYDFvZ9uPKtYCeGSRV0dxZxfmeZxvVhMRMtqdf3/B6OhZNVQ5IkkKA2496OqGo1JOKWtL1y4qDDvu88JSm7sYNOgrg1F3OA1Xt7TlkRs513c6cSxrUNq9GjtY5c46Pn6dv9Pv7asea82s39Y0WvTAxI8hxojrw9/ViZNfq1EBUsaD+QMRq2Gu/M0ZLzUL9+1cDEc+OqSN2WovGAbxRt13gi1pPOJqck0pQ1fUvPyzd5xDj3H6/ziTJSDyA1FJjliPcuHGSVnyuXzKP2+52vvA/NjkGJzUmqTHKVyekgZs1B87RcxPWWX5Qj2O0+rqosy568Lne1AN4xy85xNvC6VUORzWqZCINkiQTcX4rxWisNdK7/4SeQOR8F177wmtgjKhBNwMebfc0Quc8WnfRhxaqvJ7JU0mj3pp7OWmSZ7Ku6kySOKFWE6pRi/bQ5LVHxMiulfEo964TysVfZR9G6+R1SWZs3qCWEFaQEr9iuZXyQC7nBfCXtwsZ51kBXxukyg/vz3mTZzPu/maJTzND6mBjpMwkVS6caEiS5K2LpzvuyrguXkuxf43fB3rX1cpbejw6egVNXX/UYVDPyoN3xfNk090hV1XdpLHJZrPZkyR7uV2uBHFtw63rT0X4hrJIkkzEXeyQy+g8naAyy40bdMShGI3OctxNjafho4FWcinfEuhoXVJWfQtk3dXmM/nTGu/MEYnq2KX77Iti9eCGEzx7xGvNgOjQuWuiXJHLTrhMwRdVivD6JWUEjFv7molaZqHOJDFeJMuQJEEggj3YKw8O+fOjnhi3RjRNeHmSY1myJ3yhFkmb6crJk9YAn14JWfxLkq6naK974lnq29M3WH/8pzWik58eaidYHry7kj6Qt0fHhvHRTH3+3MnQV7y2lm002XrZExdviAFefv+WLZDD/v4tnCurvRFSJEOSZCLuRtjkjcYW7jip6+Trz2id2S4KgxGM5JsCq/vOTK+0q0DNy+enD7pU9Xsmyfn31PTjBaLVuS9e+WOj5WePZOpJmUfrMvnYyZFH93jND9t0xDwldzwipy7sLu+QJCmBaI9TEw8eced9VYxadwjRzXnwxpk4T9mC3wJc74V3JHM3CycPhgayl2FCbCb65bEG1LRsPhp6b3Xd3+dpAJYHjUq++jfN2+a4ptabj2dtpyEzt9s/z+pnAhdtsalE+sblvlCbN+w8cUl3OX4o7EwvAy+RN5vDLKZa5SDP/vLf/r+7TtHIRXsC7uYcKr7N90Foy+1i3Ddk4DeZ5zVJvpObBUQDrbKGGGkuyVuStO7NNnT9Viq1/HQRXXMKIDxLUSBngtjTgo1YuEfXnkjOR2REff7C9L1zQKG2TFdP0v4EI16rseHQBWpZ0Xub91DgTlS80SYncYXT1yGxmul16rM3H6ffVx+iIrmy0q8rD4qWvpyPz9p8nP5+tqmu7n5gXd4G1fSep7jU9dkJ6zy3AA/gnOfLwJZZ+wZwXNI6Nod45eHPlfdBGtK1mqgcqa6xHpgTkSZl84kP9pLO2bdgbOTLcdGIUsBoceisco1QzMcKB1YwMYHy54wXg9m8qWydEhlr2MyQJJVP3/BWxdusLNt7hr6ct0tUPPBxc8MwtTsir3Ec1M51vzCzQZJkIvwm8jbK9vzv68W+BJ/O2ek5SdIRIdTAePLSdfp91SH7xaUvzNzBxttonHPjBhlPh6v7TXDZxt5TjiPy/uaTvs5sgO/UReW5svleDqnWfvPaLnmDRLMEIi5nkBOeRmXyUueahWnK+qOapUjcVZH3ujFLQIXIXJPEMUe96GZap/3uo1eIUtAZm457nkny4bj4gmrVvrN+lUiHYs+lXNnifG6AxHGaOwVq3a7yFCUeqFfM415ICR7iWrhfL7kqxoou3UjxaS8rGZ/3OTbN335SrEsyyzl91wntEsK+LUqLtYS8VYlWpQtfcz7furzpE2drv2NNaKLU5Uyd9ZADA9cNayVIjEtrfl5+QIzm6TnfqYHriZ/WiMd0V5Ps+TEo6B6sV8ywmST5Jk+jknKCpXUB4e9Usbt69GDiRJZnF9SF/+H4+UNmbqPX/twUkk43N9Lfx86bBeuldhHiNRBmGQRYf1AZfauQnNMlcA69rwY9ULeYmN3kmaT76hSlGc82E/9nvyxXumQCuOPtz5K718l792nNPHGjhO/+VbrdOVu88xTNTy/V8qUUvPOwpfRB+v6Bvs4keSshNMJd1Qv5/D0cW7Q2OndIkjzECW8txP0taQtFkpTFaZ3wuSs3xRpc/n848PYlnNzzflGhYI9Nfpb629clmah5w/r0mSG5DFwdpBz/eEOqVTyXiE2cRD3bsiyteK2ViFPnrqbQzM36NzEOF8wkmcw30n4T6nlS78XevG0nxQcv5O9Wv4TX+6uDWfLmcL6eYF7VuYgzEP7OCPCgVetKyQ511HLw8dQiXL6fVuzQ231O63E5iQ3liNqf647QoMnK72n/Rx0o1BbvOk2jFilNJeqVzE1daikX78GidmLyNxBVKZwoLkT4opD3MFMXoIYLv1+mpnecbFWpgMvXeZHsx/dWFx+yhxoUp4lrDtOsLcfpg5upll8PAIFdIO+RZtPVe9cvlYdWSjM97nDyxB8rX28VUBlcrLRhuic80t7vl7UUbP7M2vC5pU0l1zJeuaw+JoAKCb37JGm9B4Idm5wbUvQbv5b+23OG1hw8T18/VItCrc/Y1bRy/1mR4HeuWSTolR7qpsCeqlg8qWEfwDPHetmtRy+KrSn4WqpZ2fwuXy+YlEB/Pt3E5fb76xajz+ftpD/XHQ369UCgMJNkMlqdQNJ8nOBZuP2Urn2SAh054oTO2/4aRsju58UdB5Nvu9ehQe0qan7dU/Ipnysbl8lr2EwSX3hXf2eOWFTPG6152/sqELwQd8mu0w4bBoaDXLambohq5kDE0//l02dswjlixwten/hptUhwuRSXNxO8rYJrkuROrWK5xD5KnNCvO6RsTA2gRc/sjry+RL2/r9eUR85dC6T/g+6f1/GbJX4PZPlC7ijrS1zii/GVr7WiLrWKZDyWlAB6yoPk14BH5F35d6H/07L9VPmt2bR87xlauvt0UJMkjoM8s8gJEvt7Y3i2HeEEScXrj4Mt0AG8qultwLmEjQepw2XW5uP01M9r6MP0Wd7WlQtQUrY43d/fvlpB8X9+r6kbU5sVkiQTU0+UvpYNcADzpdzOX6HqhpeYVf8fn/OIHQejJ1uU0QwfnmaS5NE6/n6ekZI92qgk+YuD92dzd9KX87XLJo3ywu8b6JHR4e96J4+Q+hm/fe4EF2j3QnVPinBuKvvt4r00Z+sJMRvE7q5ZxKfnJDYgTH8e20KQnELk0lNN4FCumx46fA0hHJcCKWH1VmoWalf86LanbvzMJXfPtSpnv10OR56epVzlMLhjZbqjsmNsKpXP985p6j6BfO7kTV+7fb8iqLGg0ZD51OfH1fbbuRNpuIUiqQ40NnHjBm7cw39Cm4+Er3z+velbaebm47QkPZm+p7Zvs0HcQZYHv+WurWaFJClMjp6/pnsmx9egwsnPm1M2e72fp5/PC3V5zyRPPCUZRkryM0nSLEuQbvI0msML4uWg832PutSwtLJQ8t46RaljjcIO9/cn7qsLHoMlFLN8vl7Y+Du748v7ONDROrn2e/PR8AUiuWshX1z0aOx7Yl4pfTPdcK1Hg8igzpjy3xLHJm9xR/2T8/W0x7Pnz01Y7/V+7n42JwjL9pyhYxe8dxKNBHKMcpxJcp8mFUiMt/+7bZWC9O2jde2f//5kI8rjR1MAWTAH9rmDHr/HUlIdf7+B7AtllGCsl3WNTYFVOcixacvR8FQ57D552Z7oszL5s9PtPlQ4MB68Vjee5eZCZoY1SWHwz/YT1HvsatGQ4KN7vO9j4Ovf7lqxyNv7yKCnGapW/1N2y36yeWnR0//hBsVd7hOqHgT+dIJx3wI8g7sTFbeufOGO8i63j+lZX/xBcxmTEVYfCF4JVCgW4frze8jp4y7j3nw0c7toSjGpbyMql14ilxGI/A+86gl8VxDLIT3ZdfIyHb94XTyHzx+oScmJ8X6NECNJAl/wCPHY//bTd4/WpVYVCxhehfDSJH2lv3z60qpkW3PgHM3dqqwx7dm4pGicULdk+Lp8BToDIi+xcliT5Ca2vnd3Ffs+bjJeDH/xWor9HBgIuUGH0birn1aZWCD7QhnFyEYfnHB1Hr5UxOEp/ZrYk0B7bPJz3ZjavGf2lhP2jqehtminMnhXv2Qe0WmxQek8fq3l4tjE16pc5XB3TTKt8KfvFvTlfKU5w4RVh7xsjhdjSFlcICMnoxbvdduVzNe1Uv7Kmz0LvdOpitf7dXNK5DSTJLlxg5uL6B6NSmgmULzwvU6J3BHRxlurxWy4yDOB2Q1Okni/MN7t+39Sx8eM7nb+j9apez5w44Zw1H5zrTarVzIPta9WyO92r5XTkySetTTTewLMiRMk9vhPqz3GHVuQS5rcDfKclbqg8bHeO3IZhdMjDUtQ94baTZLk+NJao+GKc4yS44q8n5/szqra3fSSExMMSZCC7VaqTZyvnYUrprYon9/h2Ixy9upNMTu75ehFh/0Tb6TvtxjIAJ7aRY4H0sJh+d6z9iZC99QpSkX93I8wUgbwkCSFgfwH8sVc7+tSgjUp4Mtsg9Yoi57mEEbgUSYuNeLOaJ7Ii2CZ1uCUfC6Oc7Po1gSDWgHjzjHufuf8wS1P9wexFljeEVy+OA9WW15eQM0jzCv2njGk3I4XofLsTbiCkZokqSWe/iqaO6uYveONop33+gLwhGcy3Ql2K/9gDQwaLVuWWHq9g/aGmPLM0MA2FbxuCeGwdtONSI9N3ChBq5GQ+tS5FfiElQe9DB77j2MfNzRS5UiIDcp7Tt2rT1239suKA6JEzYhyO7Wp0O4Tl0O+RUVamo1WpTe7aFDataGVLyoXVpIklNuBA35Tyxdv6sidpxNisALSiYvXdXcW4ZOL88B8qP4+9S7WdW5dqrUnkTyT5K6drBmm/gOlttx2xgkLJxNqoDK6JTiXwwz4bZ3YWZxnMWY814xuSiN0qUGazZi9+Tj9vVFZg5U1/Y0aSJKkBqMTF2+IkrvaxT0n6Ebhjk8f/L1NdC8yIhDxCG3FQjlp1f5zYpd2tYwQwBtPC8Pt5/4gxQC+oNQqKzMjd/FCjo+8ya4WOR7JcS5rluiNTRwj3D2vFyduEJulckvuYd1qG/pz/1hzmF6YqMS9D7pUpW4NSlBKetKirpcyilx90HvsKjrp1OQqkNhUMm92kVBfunErZFtU2Gw2+nzeLhq+YLd4nbjpQtX0JMdf/DfBv3ZuAMYf3JTCjDCTFAJcu+lu9kbPH2awRtXenLqFHv1hpa77ah1DqMYw1NzH20vFJw659MFbcpWYNVZ0rbu9Qn5R0mcFFd+cRf1/Xef1hLh6/1k640dt+j0j/hMJkjpCxLNVciJuYEWDwyia/Hd0zYCSBlYuveRuZ5AbbKi4xvyZ8evsCRJ3MVL3xQhERlkDOtyBNnXWVNb35zVu769WEQSrmoDXxP6wRHtjWrNxF2fk18ZdOZlDuZ2UAD3bqhyVzpfdtTqCotOmIxeo5Kt/iwTJU9MhrhL4d9cpn0uHD529ak+Q2Ot/bnaJG0YORsszSc4JUqCdV/l71fWpoVqXNGHVIbFtifp6ccOQQPfT4llYTvjMXnKHJCkUpDMb7zLs67cFs7JB3afAn9K8UFdEeEsWYzPHOJQjaAWmGKdRPO5aN6ZXfYf7+jNad09tJZjVSO88Ew5LX23p8/ccOHNFc2Em1/p7S6Z4fcArkzaKkUEuYdDqtsMncTVpMToQeVvfEGgnPd4hPFSBiBO+539bL14rXvc2rk99seA3kGDqnCSFqxsSmB/PmPqyX0ko+sK8O30rRYJAltPI1Q7ywxTImUD/vHgb9ZW2r1B+lu8/TN1Lyd26qGDjGYMxver5/H1aa5eGztpB3UevpO/+1a6UUK09eE7EJh7oO3/1Jg2X9viSH19OtoI1k6TFiCqHUHTIZdxJ8q2/ttjXa49/vAF90KUaGaFSIeV58Nots0K5XYhPoh2/XkLl0i++vFHPh6GuO9Wi1aQhVGuS1KfvfSZJT7md9vc6bOXjR9Dr36oc1SyeS3Raqv72HAo1XneivbGgZy2GLqTFL91OxfMqiy//23Oaeo5Z5ZBAcxK0aMdJeqZlOYeL9rf/2kJ/bThKv60+RIkJsWKfDWdPjHMcjTY2EN0KaiAqnV7uw6OQwcazbhwo+PXlDZDz5jCu9KBuidz2BbeHz10VF1q8hxZvnvhT7/q0ct9Zsd/FS20riNE9sCbeHNxMa4b471ddw2Fm7tp163mJ5JDlbQ2tcidfj45oWv+mtOHwefGt87YpMzWhTpJy+bGNR4135jiUg49eso++T59d/P7fffT0bWVp4upD4vP76hZz+N6uw/8T/5+28ajbwTR+/GB1hNWKhfLsYaCzMKXzKzMwB0MQm6auPyr2M6pVPBe93amKx/b0vuKGRDM2Hac/1x0WnZSX7T1Db07dTM3K5qN37q4qGjNxhdBjzUpTuGAmKQTkTjWeFsNq4fbGM0yw343mgvtQ527eZpIyOfYE0i6D0P4Dl0fo3HUW8oRbfLasmEyJCf7t6eROc6n7jjNue60K5Ly17lBGjfjD363QLKH76p/d9PPyA/bbxi0/IBIkPUHBqIurk5euOyy69TZaF+gsTMHEBPF/rvsO9kDFX+uP2kd7jUyQGHe9alo2n7gIeGDUcrp3xH8iGK07eF7MGvK+NWOW7qcJK5ULDrCmccsy/r69uXojlb6Yt5O2B7GEM4fBnTBDTc8ZwyHuaG3r53SjP+d53jeJ97EJ5V5EH0ozDVyl4W/+oTbhUdvTq85dvUkXrqbQS5M2io+rN5X4w+fpgb+t92uDWH+TJP6ZPPgkxwhPA3hG7C9ZMCkjNoUiSWL31SlmaIKk7jnJf+dc0n7fqGXUffQK0WDox2UHxGwgb/Hx/t/bROwPFyRJIeDv++rg2Ws0aPKm9H2Pwkuz3C5EP1v9OV5nkkS5neeyOXe/C/nmUHYj5YtiTzNA8oLfJmXz0tsdK9s/l1tvBnLy0pvgcPmLWpKjZ7NiLf62WeXZnPofzKeeY1aK5OjzuTtp8rojQS23Uzdt5NFsrdIPo3AJoppwdqrhuAbBKLzGQV0Qf1QKrKv3n7N3ktpxHGuWrMyXWd5P5+ygL+btEovHgyVbvPEbT4eUjpdTHsjTGpxzPq0H0rjB1+/1tu2GXL7Xv2VZKpFejcDy5sjiMHjpSymnTD3vOq9B4ioRbrOtGjx1i/3+3uKCO/52Xv199SFq+vECUdLHTX6GzNxG6w663wMxkD2SnAfwuPlWMO08cUmsF+JOwO2rFQzK9iC9m5S0V6zIp6DpGzImB8K5njayh2pMjt9c7b781+/vX5y+aZcZaM0AzNp8PKTHoFXex+d99dCcR8q0Wqq6CxNykhHKBkIcuDyVhXEQ7d2kFB05f5VGdKtDS/ec1mxhHsgxc8LTtkqyrvIQnt5Xy9BCGYjU9xqXjFXTWc4Y6ExSQlxmMQrLa694xC5XNuObe/DFA4+a8ePnTIil2yq4nzkMRP1SeURTk1lbjovRO+4A+NncnQ4LpM9Ie9BAdOOWxOridX9G0tUNJYMpIcBBjnDTU47udSbJy+e+8DVGeNu76KH6xcUAW5tKyfR489IOpXzy8+IY5u/+eDwwtnxQK5frDz42eeBq0prD9Ol9NeiKDzNHRs0kvTp5k/j/0Nk7xIc3kTKTxINnn8xSnk+L8gWCEv8Yv3cW7TotBj+55G7m5uO0cMcpmr4xo1KFG0DJe1qFEpKkIAokQTIbdX8d/uCLT576lhfkB5N6gtUajOIRpVvpJ1DnpEizcYOOQGH0lLInHEA8Xczzc3pFmj2SY0Wc9H3qEXeoVshtZyBvwUirPJFnMp07Xs0e0Jz8lernDsT+JFdGND3gETtOkrhMVm2A4AsuA+ERMk5SnGe2+G/p/lHL7DPF7asWEolZsLzXuar4UNeeMW69qtp3OjybE0LoOSdIZtybyJfyMC7D5cGmlFQbVXt7NpmBnpfTWwdW51gUyhbgWmt6ZTkT4uj3JzNKvuVyM/lXx8+xahH/Gxo1HDLfZU9DXiPTw6kz79il+6hJ2Xx+/xz/y+3CE5fYmSs3xGvhz2NydQYPjNUslkvzax2++tdeZdK5VmEKFn4fTe3XxP75/jNXRZIkdwXcF8Q9Hb1BuR3owgG00zdLqP6H80RA4kWUwdKtQXH7v9/oUMlejqa1LuTNuyp73ffI1yATyjarHEC8zSTJ5FcgTnq+ajC9q7r2juz+BgleEyfj2uFApr793SaJa5N9ZUgwSh+xO35B2VPM09ok3kCXN7P9bdVBeye5lydtFN2Yev6wymE9lVrKoCZIPIPU7/ayFCpqdyTnWUJ/y2Ig8pmtvbS7zb6d8d9m5cGz6Kmf14qZ9mA2e5BLyn7u08DjfbUuPp3Jp3c9+wEGkiP5ejHvLUd1iU02N/s/pf+bZ578xcmvM+cS6LenbQ1oJsmfzqu7/dho3Ii4xBUOPCPFrzmv1/F23uYuf/O2nhB7RXGJHsciXmvcedhSUVHgbObmY/YE6cF6xUTL71BRu8rK1G0xwgEzSSbCb/qbJr1I4QkAtU3j5iPBbSUszwjJXU20RjrrSXtQ8Zokd7i8iGe+HqynfaJ2aB0e4pmkO6sWog2HM17TXx9vSA99t1xzNFV+DeSLCPUlM+IE7M2hc/531Dl9+Qb9b84OerB+cV3d+PiE7m/raiPLGnhvjnembaF2VQvR5w/UtH+dk6ZvF++l6RuPifJaeV3HoHYVxe2MGyUMnrqZPrm3hv3r6t9Rg1J5aGyv+hRK+XLEi73B5BI7vhDhNUsl0veuAGtxN4PON4sZ+1D0/ZZwWa98XnSHO5zxoXEp6QP1HLucBTM2NS2Xz2O80TMrLL/mxaQ1pvavu9yfQobjIHdR44X0qrc6VqZ3pm3VTGLl2CTHULWiI9Buo3r4s7efat2h8+KjT9NSun53mw5foK//2RWWuMTvm+SkeLEn4WdzdtLUDUfp43uqi1JqFZevfThju6gaOHAmI2ZzWTeX8KszNbz/UdkCOahTjcIuG0m/1LZCSAfv3A3ghbPKATNJEThy5syf1s+BlDtdT0kLS7DWitHyidl5nyTZ5KcbU68mJemRht6TpFAGIr74eLxZKVEm59zeU5PN3UieGoiCX8f/pFNbb19w7fjX/+ym+0YoLVq9afnpQuo9drVfP8vIBbLcppTf93+uOyISPdWyPWdoyMztYjNEvojkTTnVv0e+XcbNGeQFyOqgQ5XC4dlbSysYhXPEDsLL3WmPL3j1zHIYTWtNqbe/c2/raEJVApgQl0l3+eKwh2vTwDblqbrGHnvOscifzqv+4t+5XE6nzmDIX5fJz1b+UmwIk6Q+P/oXK+Q1RSM09lTSKqPu+M0SmrP1hM8/R44fgSiUqMQZblTBVSAvSpvlsmEL9ohKEDVBKlcgh+gkx533vpzvmNz9ufaww+fqwGTlwr6XmAeqjMaa5yPnrjl0OgwlJEkmIq8x8cWPvevR863LUzDJF3fB3h8p0c2eCt7aMMvlZ854PclbHau4ba8sB59QziRlSt8zobO0s3rWLJndPme5JM6xk1/oZpKMIHdY8ySQ8glDZpLSkyTZP9IC5bnblCDZsHQesZkvLzKe/0ILKpo7Y+Bi5CO1KXe2OJFkybOwW+1JUugDESuWJ+MY65VU9lLaJ40ag8V42ENOb8LibNPbd/h9TnLuaOaOQ5OAIJ+7tS7g3B2T3vK2DtULie6TWoODzklRIDmgr3GbkyCecS6VL7vmOdU5SZLL1bRmkiIlNukpoQuk2+npy8Y0yElOr3KQHT1/zf7veemx6anbytCGt+6guQNb0ERp2xD2Y2+lgmH1gXP2awtOANXBsiphiE3Os3ic2PGhhWK/Qi2R8a6NMmfc/JH4u48Br8Xp1VRpoxgsvDgwVKNZlQvlFF1O5HbXTCvoyCfqQEYRHeJTiGeSmFxTLHd1cp49u+qmWYZ6/KEYrYsURpbbybgxxoLtJ0VpBy8wZT0blxIzSHyhwyf519tXsq9haFUpWWyap7bcVi8oeANZFsii5kDIXQrVstVwLpCF8HK3twuf7/2dSeJF2d89WtewdSha5LgQ7AG8d++uQp1rFqYJTzT0eD+ORYlZA1/N4DKTFOJScHZLarYjJzrOa4DlPYliwjSTZISkbN73OuT1p+FWSCM2/bbqEP2z/YQ4j3Oyx7/Dvi3KiFbb6mDxw+lrvh+qX4yalMlL2bNkFn/7u04qa415zTH/TRXIGU8Fcrr+jFAok15NwyXhapIul32GUmS8a6OMenHkLM7PQMR/CLyJ6awBzShY5LVSwT9Px9Cg9pWoZ5NSDrdqlS/wPkJcqsaldIH9RArbTJLzBatcdun8lHknaq0RHjVxdTda90Ib/2caH21Uwq/v47U2waLnV2RE6Y1WksTtj3uNXUV1P5gnghH/vngPK1m7aoVoev+mNPmpxmLwo276TM3qA2fF/w+cvSparPKFgxoQQu2e2kWpapFE0RxFDURIkqKfrwvU+W+NZ7v9xa17X7yjfNBmkuTEKNgN+rgS4YsHa1HD0o5/71qDX1y5ULt4LvrqoVqG/fxMYRjA466barmWPJDrvDyAE0j2bMuymrN7wZhJkhs3GYVnz7zxtEltKJY/sGSNKgcuo+Py9Ns/XSg+r1Mitz1BUr3bqYooo+T3J/9d1yyey2EATy21C1eFAxvcsYq4thvZvU7YYxOSJBPxd5RILc/iRClYUhxmknz3jE+L/7QjnVZs59dsWLfa4g9eObbAo0goq+/VX3mFgjnph551acazzRzeB86JYe7sWWjLO23pr2eaaj6Ou9G6QPK+wXdVdvv9H3XN2FnduTxNLht05nzi9pWn0kojyUkSl6fJ3a3UXw0HIh4xd8YzRGp5Z90SSpLE7cC5hFItu+NAEMgFaCDy54yn6f2bieYo4Q5EEDq+NmDgC15ugx8If0akdSdJDjNJvuOF+kbj83ThXFlp8tNNHBbEByqgmSQfXxx1kOn5NuXp8wdq0K9PNHScSXI6b91RpSBtfPsOGnhHBTeNG4xdL8sX8XInXFlhjcEtPt9p4ZkUX8r6mboBt5ZQrd+TZ5LaVE7WvI/W3kL8e+MtKdSyNrXKgWMTU2NTuNbKqsc9a0BzUeFQEkkSqLizlD+4W0mwZ0C+WbA7oO9vVMbzyJsecsJQq3gu+vLBjC5jgQr2ZrLujlU+H7esmOyyUFLrhM37RjmfiNXP3I3WZcviX+kHB3g+qbqLG/Lx8ojp6x2UMjMmjzoOaF3O50Ail3g64yYd3IQjp5tNCnnU0wjy4yfnTKDP7q8parzl3eiblfO+yR0nTFz+x/Xo3B3qjSnKPjXVi3pvExwKapLE5yDnVuUQXXzdD8aIaz5/zql6y+1mSPvC6bnADfbIf41iuehrg2aPQlHU0LJiAc3b1RkgvpjuUquomGFxmEnSeGOoA7VajRvcxSb1+sVXn9xb3e0SBY7n8sbcP/Wu7zYmqDMpvgwiXPGQJHELa64O4GsUzeO+pzoZPZPEW38Mvbc6vdquIj1QN6PDYzMPHRhV6gAeNyX6bM4O+n210sRBq5FIOJRWy+2QJIG/1BGaYA5g/LtL2XxS8OPn+DK64i7Oybf/+XQTurtmRrODQAWz3I73GehYXXs00VtQ13s9oyZ57nbFfrB+MapfMg+9cmdFh9vdjUCpvJVIVC2cJE7EXN/MCVU2aVROXhPkPIqo5+2w4bCyh5AWDrzvd65Ga95sY2iJoKfkuXaJ3GLWiF/DHo1LUveGJUS52n1S21V3+EKjWnrQGfvffvvC33DtIu6Mu1Ylpl+s7DmFTWWjmbzGRA8j1sH4c07VO5Mktwn3ZybJyNM9lxjzxpjcUtkIWs15jMQbg9/uLknS6LYrl9h5iulyqbP6b3fNDni/KT6P8uCTjNclezunujsGftl4IIsTlk/vq0HNPZxnnRs16BlEGO6hA158XGbxuv7Rt7Hm13kWxwiFc2UkSbWL56b76hYT649ev6uSiFMdaxSmajrWu3Iyp77NvvpHGQznOC5vr2KGAbw9Jy/7NQgSKOyTFEVCtajTn/epu5MZl5hxDS3/oa5L31jTHT0tVf1euCu3ACdjcRDiQMEX7j8tO+DwNW9PSW8bWfVXz51g3M0k/Z7e2YZH1B77abV9Pw+Pj+vl5/LzGidtqigHRzmgrj2oTOXb76fxXuUZDG6/yokbz7zwyJY7l9JH8jiJ4x/pHNf07HOh1/ButWnhjpMus2Hvda7q0+P0bFySdhy/JEorefSWy0Vuq6B9gRKOcwePgPNgyH+7z4S11AKC65bOGRqVv2FFLk31pzpWLf/xhZEXUXzRzucjLrPl/Wj0MHqAzaGfUICPrfXKFM+TjQrlSqCRC/e4VLJodQqUn5+nMmGH+6XHBHdrXPi8w2W/nJwcOHNFbLfgKZbpaczDP5/3e5vzfAv7be7eGicu3vD697H/9BX6Y+1h6tWklBhQ8vTe5Bk2T6+NpzJ0XxRKyirWf3FSVixPNofZvD+e0k7QtHCp+MP1i9Nf649Sk7L5qEHpPKLyh0tGzYDjJS8j4D39dpy4RBULhnatFGaSItxvUpedUNXC+rOpoLvg0aBUXtr6blu3oy6+Jgzu6o5DGYycqcHm3bur+pzU6X2lfQnOraXZI3nmp0L6vjmVCyX6HfTlVsFyKYTzYnGtxx2+YLdYeHrX10vE59U9jILJb4VqGiVrRiZJ7asVEpvA+luyqOKRvc3vtBUBjDfoM0uCpLo9/XgW7sxocQ7Rx12HTCMv/Hkke/zjDQN6DH/ijJEDzTxjzH+vT7XQv57WUzKoxiae3fBHoOFd3uPI/piZlItqrW59WtcT8q/R076O8reqv3utpEaeSeef93D9Eg5l5c6xSV7f5WkfPLltua8DqFozrV1H/Cf29+MNxRlXZbhzyss+SHL32kDx+i8jNnv9oEs12vROW9EogRPBUCcinnAsb5y+XGPBdqWbbCghSYpgPRqVoAZSl51Qdb6Rmzjo5W6fDT4x8sUnz0CoF+wcYLXoiZk8Ws8tLtX+//4INEca06uew6a1npLXNINnkmQ8SzSqex1a8OJtupLKbg2Li05n3zxcy2vQ56DFZRLO5NFHT6V6Wi/J0j1nHD7feUIpg/BWMsAzPffXLSoaTAQjSbIKtexm5b6zYmHysQvXxG7tEF2uelhPocWfuPJcq3IOa31C1THUj7zK42w5z2ao62m1kgxnnp7nL481EKW5o3vU039sDhUOgb2G3P55ULuK9j3R5DbeWjFK67k4zhDpm0lSH1ueXeEYzet0v5ZijfJ9pBk/eNaej533k1LFZ9Y+x7erWpA+1lj3ozeB1krO1cYl6gwSz2gwT2V86u+cn2sXaR9EIzY5t2psWrhDGcDbdPgC7U5vWR5s+G1FkVCV2/la0+4pUZBPpv++fLv4KKDR2lJvKQUnXB92qebzWg/H2u+YgEfkX2hTQXpw/5Mgd40JnGn9iFSbjdpWKag5qsYLPHlh6/3SIs/c2bKITmdyO3J3FwYzn2tGTTUWhcq/5zurFrR34XF+nlrPWt4riv2wdJ+9ScP+jzqIUV0tfDHGMz3yQlNvZYTgit8nJfNmEwvmudTxnuH/0cPfraBlTskrRDY+L/jCn/Oh8/k+VElSqh+xSc/fxT8vtPA42KQnBpdPzklD76vhUBrl9fGkM7sRL+GTLcqILTNU6q9JKz5r3SZ3T/W0wbDWPkny/Tn55DXFzh15edC3bZVkkQzJ7xkelONjV9dNsrhY15/PSeiIR+pobt1gc4p/TGvrkFQP5aicuHGXNXV9FTdJ4Ngkb7Ehl+tx+Rpfj8iDdpGyX5SZ3Fa+gH3TW27UcvewJdR1+H9i49tgw5okE9FaW+Hr94fC9uO+Z/DugqR84uR2yZ564AVzzZ5juV3gjyc/3xg/nhOfWGduPiamvn39eXr2Q+EFnvzBG6JqlU9wy9dpG46J+4jnEJNxrPuGtHd7McAJjXxRsPTVlpQnWxaxUd2C9I1X3S2OddepaPsx5f2W4CW4yM0hEjBa5xcuZ+HFu2+md99jy/aeMaQ7JZiDr3mE3vOhfI5w/p5QxaZglePIA0eeGP08HWaSDHps+cyrnse14odWksQJIyciubLFedyHTqsFuPx47r6T7zOqu7L58PgVB10SIh5A5QoaXofjXP7ctXYRkYTqwU0h7qicTCXzZqfD567R3K0ndJV5ctzacTxjn8ts8UrMuX4r1eP3ywO8odwQOFoUz5uNahbLResPnaenf1krbrt4/RZtO3bR3sI8WHAlYSLuLhL1CtWapC/m7XK5jXch13tsvFDTnw0/9Zae+UM+bxnS8lbnr9Ld7BhP0XNDBLku2/MPJL84lsRlPAi3fP2hZz37/j8OSZ+Hk7z8NU6AeZaHF6o6t7p2TpJmbT4mRom0Xhetcg1vzwXldv55+vayLmsmeNEyRA9fz6N6Z4HkAS/nRf9GbOysx2+rD/kcm4y8aA1m4wajHlvr16+1vkh7TVKMSERe7+B5E1etxg0Oh+/jU5HXM71zd1V6rX3GVhMqr29rm+Pz4MSX35fOs0nOs5GvTNrosNSAEzTnWaOHG5Tw2JUxmNcuVvHxPdVdSvj3ngp+bEKSZCKBTsOGqqRB82d7CYLy9a2/HYgCmWXzxrHeO/DX0V1SMaJbbeoq1Scb9Zz8/d3rTcz9ucbxVLPOgWTDofM0bMFusedE35+V0SFVuddnupTtids9tNaV/36QJPmHX7fvH60nWhnzRrds72m0BI8mvl6wnbzkeSG61t97uMrttIQqQRM/K4g/KpjPgreO6Hd7GYfGCFrd7fTSatwgx1ifGwLpiFPe3tfuGjc4D9jdTE2jvzceo99WHaQbt1IdEu+jF65TrzGrXDZ0ldffae3vhRzJmC53wx+uTTWKJtk3rQ/F3kkot4uimaRwzuJ6O+nJX/d3VCWiZpLc/LtdtULiY3J6e2tf1wfo+Xn223Q8D09tVGW8Yeo/20963XhRfjpa+2yoeKr87mFLxb93pi+ClcklC291rKxr0av8NaParFq1tOG3JxuJhbGtP1tM+05dEQMbKBOx5mayeskzSc6JSQjzFJ9jk7Fva6Pr7YIwk+Tm9pfaVhR7pP214ajy8wK4HJHPFWrC7NiEwnjcmMITd6G2rlO5FrcgV9uQO+/v54y3qvC2tjbYA7xW0rpysvj4Yck+enf6Vtobgj39kCSZiN7N89wJ62idlx/tkCT5+zRDdKIxevNETw+ndUL1h7+H7LDpn4fH4A35fvxvP93rdeNUm9uFvdzhR2vvo1mblYDkjlrypxyj+4OUmzXkzua4IBh8xwvM+eW+cjNV7FGRL4d/7fXBXIJ1wSYPiriU25k4Npn5ZzlUOBi1JsnDwJw8Gx9I+b78rZpJUoxxTTim928qup5pNWGQuXvWngbUthzN2Kg4EOqsPBijZD6l+clBnfuXBQJJUhD8ue4wHbtw3efv412613rZUNWTQKbHg81hpsbPEapQ1fUa8Srq/VUYlCMZchGi1QVP7nL3vNTBxx15DZXzzKi7I7zhQ0v5RqXz0sbDFzRr6LlkhBteZI93XdQLvuNRVL5oup6SJjb6hcifQfp0zo6glf44zCSFaU2SFm8X+75urhvSNUlBnn1xJq/58FQu7Y1WC3CHZg46Xqfied3HI+fZHK0ZHZf7FU70eYPi7/5VOqzqwZ1oeZNzdU8f2SMNS9Cl6ynUpnJG6Tj4Tx04DUVcwpVEEDz/2wa/vs8q5Xb+JnNKXXJw/iiMbAHu/BiB7m8RbLyXw6lLN6hc+oZ9geBWrY81LUVlCuQwpJEI7wgvG9C6vJjRkDfEdW54AcZR37uoqY983MFrxMI9QXt8+aLa+frazOV2RpYfBlKi5rVsOwQBXi4vC+R3ptm4Qfq6noduXi4fvd+5KlUpbMzGprzpKicx3aQmC4Hg6grZ3882o1lbjmk+Ps9W8cavYIyYEA6co3GDwQ6fu+r39wZ6DgznmgH5Z9cqnotmPNuMPpE2dJMPzd+L5wfrK3v6yPvhBCcYGfB4uksLjPkjDySx470cOkvNJAJ9H7xxV2V6qL5rsvL07Uo7cV+UyJvNJdg83ry0x1kvMI76p4okKfLN2eq5rNUd3pxTD/m8bqrGDdLPfrJFaZo9oDkVzS0ttDdgb6XK6ethuE1xsBjWAtzD37JcbmczbI2vVne7GF2xhGdgahXX3lzeV9w+/LP7a2puVv9R12o+P16e7HEuazmfaF5Gf0daCPh6MxRxCUlSAP7bfdpl0fmjo1f6PQJfKp++vRjMyPmkWLlwosNmb/ymVhf989f80bd5GXq9fSUa1b0OGc3o/Si87ZOkljU0LuO6Ias/TFxpaVe2QE7aMPiOcB8G+PE+RgvbyHHzVprYcPHslZsOt09e67oeUI97antbh+i6R5pztcDuk+HrkCgnbLxukbtkydSObvVL+b/fytD7qosGMz0b69vXzp/BR6NO8e66vDk38uHNxf0ll1dmvP7GPxejPFi/OH39UC2fvieQckQIjPqWCkVcQsrrJ+4C8/D3K8S/ecdllb8tCfnE9VLbCvTryowN1CKVegJ0rEEmsWP5rbQ0+nzuTr8eN3f2LGIWIdiMKI/zNlnGO7iv3HfWoeVqYD/PbGFHm/M+B2Bu6tsKSVLk+HL+Thq2YI9olz93YAtDGjK8fGcF+mTWDs/389DdLpxJkuO6HtfzZNHc2WjT23dQ9iyxVPq1GX79jCqFk8SH0YJRbufpT5l/b5P6NhLrEHkdarAaN5gxXvkamwJdHgH+U98/oQhLSJJ8wJ3IuMtTcmIC7Tph7Emfy6IDOSmFm+a+CE7rfPgklIUymbJ0x4jGEo6P53nFLQdm/jCKCWOO19FmPdB2OrzU1x8tbM3twrUUcQ7mBc3TNhwTt+0yKjGxEdUqljugxg1Ny+WjccsPUDjIs1ruBq/kDppmEozTn7c/5bol/Z9R09u4wYynda1mQEbeH4yjvn/83XPTF0iFffD0L2upwYfzadX+s5qnGuV2a3IIijGuAUk+nZjxgkseYTS60UIoGjdEyuna16YdoTgJgnsZf8P4PZh58K7e+/Oo2ttzRKmdczlVWprNvveNP/jx9CwjddhM1unvvEbR4K3V8Uae4TLjxbn+uGSMUJxStdYg+9q4IdQy+zg6ipkkM5SBm2QmqWvXrj4/8MiRI6lAgQIUTeZsPSH+/+3ivdTVaaE773F038hlfj/2beXzUyTTOgE6nOAN2Ew2mIzeTNbdYwdNhER/uQyHFwl7a/+NmSTrBCN/IDYRXUtJpZvpewlw4yDn0+u0jUfpuQnr/X78Yrmz0flrKT7NEjtfbxrR6dKIc456PomY04rBa2VDRaubrWO1hvmejDwTWjp/dtp7yvPSCSRJ1igD1/VbnjJlCmXJkoWSkpJ0ffz99990+XL4apBDMXLn/KtxbuDgqxdM0h7ymdvLGtZC2+hmCMFkdHe7UNM6ZLO3HvdlfyQId7mdObMkxCbHc++tNJtLkuRtLZEnr7WvKLYG0HNNq1ViZWRp0tsdK/v1ffKsViSe2zMYtCYpBLPCjqV16kySuV/8bNKmsoWTMrofuhPOxN/qMqlrkshEa5K++uor3aNvkyZNomjGo6rOgajTN0v9fjxul+xp12df8HsnkOsZ3tDW35/r/O8YNydNs5dQGT17EYpTaSSdr7NnyUxXbmJz0ogasTNxPmv12CSfT7WS2SPn/d+VXu1sp+ec6GlNkhHnVN4s2h+OZd+RccEe1DVJtvCsAzP7oGnFghlddwskxof1WCDC1iQtWLCA8uTRv5hv5syZVKSIMfuumJFNYzQmkA3pjLzADfQ94+80uGNpncbjSreZsnQnCK1WVXlzBL8hh9ZFSChGDP3xbKty4v3w2f2Om/FpaVUxesqiIlEoW636A7HJcTSVf01GXjio64z0dCNzKLdzSZICPxa/Y5PGTJJZz42hqHBw3qA7GGIyaQyaaiSrZsID1U3L5qOcCbHUo1FJr/cvmJgQkuOC8JaB60qSWrRoQbGx+hvhNW3alOLjfcvEP/roI3EyGzBggP2269evU79+/Shv3ryUI0cOuueee+jECWVdULgZec0QqnaYzXWse/J1Yb3ezePkk6IZL7gcg5Exv4/h3WrTO52qiP2Bgs18Ice9J1uUoQ1v3UFdnfZfaVYuH/3Qsy4VTkoQ/x56b3Xq3tCY3dHBP2Zs1StDbHKNRUaeXbn9N4sJcDNZI95F/sYmh9bk5n47h2RNZodqheiFNuXpl8caUGjOG+r1gPl/Dz/2rk/LB7WiakUc27lzHPr4nmqUlDWOHqpfTLx2SdnM2RHRWpuc28zbAvzkyZPiI82pDqN69eo+P9aqVato1KhRLt/7/PPPixryiRMninryZ555RizUXbrU/9I2I/Avxshfjb/nwVQf3yC5snr/o/Z3LaLWU5APTx5ZMl+KFJzGDe2rFaJwXsyacbTOU8vdcX2UoL301QJo2GASkbiZrOVik815Jsm4h1YTEz3JstZifZW3v2c9ZeL+xiaH7naRVm7n5t+Bzsj1b1XOoEdz8zO8lN+b9fTO75Xs8Y6Xxc+2LEsD09eM31+3GGKTxbam8DlJWrNmDfXo0YO2bdtmz+L4gPnf/P/UVN/WGvAi2m7dutF3331H77//vv32Cxcu0OjRo2n8+PHUsmVLcduYMWOoUqVKtHz5cmrYsCGFy+YjF6iLU3e7QCTE+bceKeWWb+8QXxffBtpByN3j9m5SksavOEh3VQ9dEuFbMIrAk2AEHrI7CELm8Uq7inT9ZioVyeV9IXO4WTU2yUN2249fNLSUTKtUSk8ccz4GYwaeAq9y8PY8uDnE29O20qf3eS8FDrVIOi06NG6gyCi3c+dGeudIhthkDvlzxouZvVBsTu9zktS7d28qX768CBLJyckBv2m4ZKFDhw7UunVrh0DEAS8lJUXcrqpYsSIVL16cli1b5jYQ3bhxQ3yoLl68SEY7dzWFXpy4wf65P1N+W99tS+OWHaDJa4/QUy3K6P6+NzpUok/n7BA7Yjcpl8+nn6knAfK3Y4tjkwatk2IGLj/b8k5bh24y4aZVtx5JInVju/ql8tDKfWepbgnvm1VC6HWqUZgihVVjkxx+Bv6eEZf80bpSsigT7vvzGjGiriY+emLHsy3L0dz0bTKcB/68XRTzV71HUZsB3e08H0fPJqXovrrFXGYTwiUSY5Hzcautsr2tWzar8iEolwffJCbE0QP1ilMo+Hwm2Lt3L/3xxx9Utqx/raJlEyZMoLVr14qSBmfHjx8XrV1z5XLchI6DH3/NnSFDhtA777xDwSZP8/kz5ZctS6xYm8EfnnD9668rD9Erd1akbg2LizcHX7hsPnqBbitfwPCFr37vX+AmIbI/rtNZ0SxBSEskncBVWSJ0zwa+IPt99SG6t47j+iQAX1k1NhlZccJ7l/Ho7A896zncrrXPZnJiPJ24eIPmDWxOyYkJooR2ev+mImlzSZK8nFNF8uJlsNHfMkJvsxrOzBSbgrnJeTDJx6qO9juW4Jn/uUx7pimt3H+WOhtYNQSRx+ezQatWrWjDhg0BB6JDhw7Rc889R3PnzqWEBOO6hAwaNIgGDhzoMFpXrFgxCqZg1uu/1bEK9Wlamsrkz24/sRRITKCWfnRW0ZP/+Ls4VmtfBJnZz4mOM2EmP1hJ7mxxYmazTeWCFIny5Yinp28L/KIWALHJAG5OfVrnxAUv3kZnLt+kYlK3tKpOC96N5O/6A8dScIookXa8WhUpyTkTdA2kmk21okniA6zN5yTp+++/F3XfmzdvpqpVq1JcnOMC7E6dOul6HC5Z4MW1tWvXtt/GNeOLFy+mb775hmbPnk03b96k8+fPO4zYcQehggXdXxBy5yJfuxcFasq6I0F7bA5O/u5dpPVYoSy3c/i6yc+KZt/DwZ3Zzzen9QfPizIZACuzamzyVO79xpRNhsSIGDfVENny6LuEMOKc6u9aK7kSOZIGwJxF0qHzdQTPKt5MTbN3gYvUcjuwNp+TJK655g4+vN+EM18Wx/Ko36ZNjifwXr16idruV155RYywcZCbP3++aK/KduzYQQcPHqRGjRqRmbw0aWPQHtvIVpkl8mbX8fPc/8C7axamqeuPan7N8ds8N24wI/kEbvZjlRXImUB3VInMWSQAI1k1NnlKHX5eftCnx3J35gu0PMrbObVR6by0ZPdpj/dxlws+0bw0TVpzmM5euRl1M0mySDt051lFd2uUAaIqSerfvz898sgj9Oabb4oabH/lzJlTjPbJsmfPLvadUG/v06ePKE/gzQITExPFz+YgFM7OdqFmZO1um8rcWplEZ7mDZ6/6nJTF6C638+17TSEaT+BR80QAvLNqbDJ2zz7fbtfL27d/8WBN+vG//fT1P7vd3sfmZsAuNpPn1Tpaa5IihWOFQ6QdvSPHFuCR/VzAOnxe7X3mzBmxR0QgQUivzz//nO666y4xWte8eXNRyjB58mSyEiNnkjhY9G1RhpqUzev2PvLJ67P7azg0BvB0YvNWpBFJszNRcwKPnK1tAAJm1dhkbMtv7XNfoOdvb+dUXpv4QvpeNHrKCr98sJb93zkSYj3OEDmUkKsbnVNkiKRmDd7I76FQbAIKEJaZJN4wb8GCBVSmjP621XotXLjQ4XNeNDts2DDxYVVGXrDrCXTOa3MmPNGQ3pu+ld7rXJV+Xn5A3zf6/mWTjXKF8UAAwC+WjU0GXm+6O/cFnCRR4Jyvq19rX5FmbzlBPRqVpDFL97v9PjlHMnLQMRSiKRZF03MB6/A5SeJ9KLhLz5IlS6hatWoui2OfffZZI48PAvRwg+KivE53kiT9m+/fsHRe+vvZZuLzXzzUt3tr1hBJszORNOsFANaOTbYQzFwEekp0/n6uTEhOiqdDZ6/5PWP2RPMy4kM8vsefHZlttKNt8C7SXnsAv7vb5ciRgxYtWiQ+nE9G0RqIIlHHGoXFRrVqkqSeZP2d6fZ0ktY6AUbqjHrUnMqj5okAeGfV2BSKNUmBJ0mOD7D45dtp6OwddOjsYd2PkZbm6fH1biZLESuSj12I0EFTsDafk6R9+/YF50ggZK2tPe3rJH/F+UTmebSOIprj8w7jgQCAXxCbzFtuZ8RaKpufsxTymqTIK7eLsAP2IIqeCliIz40bZLz4DgvwzCvG6SSrBrpUDyNy8q8zxoeTXCTujeRAft4RdeAAYOXYZGTjBnfJkNFJkng4Hw/b0+/TY2ySW4CnR7VIOcdHxlHqgzJ2sEyS9NNPP4ma76xZs4qP6tWr07hx44w/OgiY8xojb8FG/przOc3TSS5Sgo47Ry9k1MZH+FMBsCwrxiYjc0F3575gnBNtBt7fU2xyGMCLsHO7/LR8Wb9lRhH20gP4V2732WefiX0onnnmGWrSpIm4jRfK9u3bl06fPi1asII5FM2d1eEkqwaLQrkS9JWdOZ3Wojl5OHwuIwBhxAsg8lg1NtmCdB6UpaYZOyvHjRu0xGWOoZRUNz/Lz0OQ1yRF2rk90gcfZVH0VMBCfE6Svv76axoxYgQ9+uij9ts6depEVapUobfffjtqA1Ek6nd7Wbp841bGDeknqadvK0snL96g9tUKUa+xqxy+R16vVDp/dqdH9DSTpOdekSHSjx/AiqwamwItK0yIy0TXU5Qa7H93nda8zy13iYufcmfPonn71H5N6bt/91JKahpN33jM4WvlknMYUG4H4YLudmCJcrtjx45R48aNXW7n2/hroG3mc0obbW8Gtato2M/MHh+rWW7Htw+9rwbdViG/5vdNe6YpjepehyoVSvRhTZK0URxFtkgbbQQA68amQMvtvnu0rtf7FMuTlVpVLEBGeKh+MbfJXeXCifT5AzWpToncLl+rVTw3jehWm/5+tqmPnVf13Q+CC689WCJJKlu2LP3+++8ut//2229Urlw5o44r6jgnHO482aKMoYmSp4t/nsoffFdl6ne7tPmijaha0SRqW6Wgy/d7OsfFRlrbIA9wMgeIPIhN/smbPd7rfThWjO5Zz9Cfa/Oyv99D9YtTXadkqV21QlSlcJLr8XmITnLcwwBY+OClB0uU273zzjv0wAMP0OLFi+1130uXLqX58+drBigI78lEDkRaeUzvpqVEvfmwBXtc7u/MU4CJk2rM1XtF6jkxak7mkT6lB+ADq8amQGeSQn++UxsIub9HfGxmGtK1Gn27eA+tPnDO6yN6GqPT2gYjWk7xkUROZK3SeRIsOJN0zz330MqVKylfvnw0ZcoU8cH/5tu6dOkSnKOMcKXzOa/tCR3Hlt7u2rtq39+nDfs0olTZAu5ryM0MtdMAkQexyXfP3F425LMrvvw4vediTw0OtL50V43C4v/lPaxzAmNFUbEJWIhPM0kpKSn05JNPig5CP//8c/COKor0aFSCBrWvRKbgtr2rvJ7Iw14UHh6auxI5K50/B/36eEPKn1N7ka5Z4WQOEFmsHJv83SdpeLfa1K5qQdp98jKZld6EyuNG59JX1YSQk8PKhRKpQak8gR4i6IRSR4j6maS4uDj6448/gnc0UahUvuyUEJeZIoXnmST3J7nb3SzqbVQmL5UtkJMiSTS1XQWwAivHJn8rl8on5xTnunCd7/Qctu5jc3O3CuI5yo+n/D9LbCa6s2pBt132wHgIq2CJcrvOnTuLMgaIjAtueZRRz6HILcD12vxOW8qnY/FvpIix+AgzQCSyamyyBThjHq6Zcz3rUvQemrv7cSc8OQajlDp8HCtWAKK0cQN3CXr33XfFgtg6depQ9uyO622effZZI48v4vkTgIw8kTuuSdJxfz+my3PEx0bVQkyMeAFEHqvGJn/Pver5XD6vv9CmPAVbjE8zSfoe011sis2cyWkbDH2PBwDgV5I0evRoypUrF61Zs0Z8OI8URGsgMnImqVm5fOadAfCzcUO4Z8yMFC3PJYryVgCvrBqb/P0zV09zcoLRuKzn2BTqNSq6Z5J87G4H4YXYBFGbJO3bty84RxKl/FmsmKpsfm6IQkkJYmO+LJkziRkfb/xt3CA7c+WmD0cIwYI4BFZi1djk7wWnGpvkEBWKmRb1573ctgIt23OaejUp5eG+OrvbBVyYB6GE2ARRuyaJyxmuXr3qcvu1a9fE18CR1jne24k/Nc24LIl/1qS+jWj84w10BRx/W4DL1ujY1wKCL5pKIAG8sW5ssgU2kyRlRlpbOQRLsTzZaNXrranf7WXd3kd33wYP9zt87pr937cMjK3gP8QmiNokiTfsu3zZtWUoByf+GjjSijnezvspqcaeQHzpYORvdzsAgHCyamwKeCZJ47ZQ8RZTYgx+fdJwbQ4AwUySeARA68S2YcMGypMHew4403qtvMWhjukb3VUtkkih0r5aQSqRNxs19bBeCikSAJgVYlPga5K8JUlvdFD2/BsYQIMHXxoTda5VRJSM31+3qOfH9HDc8kClkVUaABD9dK9Jyp07t31Gonz58g4npdTUVDGC17dv32AdZ8TyZ2SubIEctPqN1pSUNY5CZdjDtcWIm1x64QJZUkTBoClYgdVjk/8twNXudtJtXoZNH2tWWgziFcgZmi0fcibE0dJXWnqOS15CE++JpLplcJUGAEQ33UnSF198IUbqevfuLUoXkpKS7F/LkiULlSxZkho1ahSs44xY/uYV+XKEdt8h5SIjpD8SACBgVo9N/pbbqed7OanUM6iXnJhAoeQtQfKmRN6MVvCpEVxv91D9YuE+BADL0Z0k9ejRQ/y/VKlS1KRJE4qN9bkxniVpjcxFai6it0wiZwLeG2aAtbFgBVaPTf5uGaE5kxShwUkrt+tQrZD4P3d3jQaFkrJStEBsgqhdk5QzZ07atm2b/fOpU6eKnc5fe+01unkTrZ+dhXohbDA5P5XHmpaiWQOaud4RJ0AACDGrxqZAGzeEvllD8B/z7Y6V6f3OVV3uh9BkDmHdCxIgmEnSk08+STt37hT/3rt3Lz3wwAOULVs2mjhxIr388su+PlzUi6aOcM7P5MH6xaliwdA1lwAAcMeqscnvcrv0/4c8SQrKYzo+as8mpSh39iwUTaLnSgIzSRDFSRIHoZo1a4p/c/Bp0aIFjR8/nsaOHUt//PFHMI4x6k5sodyLwkjOsdRdbMX5DwBCzaqxKdByu5hMkV/5oPewcXEOAEFvAZ6W3kZz3rx51L59e/HvYsWK0enTp319uKinFXQiNknSOZYVmc8OACKZVWOT3zNJmVzP15Ebm3TeLzKfXsQfO4BlkqS6devS+++/T+PGjaNFixZRhw4dxO379u2j5OTkYBxjRNOKOVEzk+T2jiE4GAAACWKTnzNJPna3MyO9eWJkPrvoK93HhB5EbZLE7VbXrl1LzzzzDL3++utUtmxZcfukSZOocePGwTjGKNxMNjpOdu6eR6QGWgCIXIhNvlHP0mnSVFQoBvCCEf/0zqYhNgGAL3zulVq9enXatGmTy+1Dhw6lzJkz+/pwUU8+J99VvRBN33iMnmhWmiKRc3hxF24QhwAg1KwamwLtbpc9Syxlz5KZUlJtlD9Em8SGDWKTKWBtGEQKwzaUSEgI7QZzkUIeufr6oVr0QZdqlJQ1jiKSU/bjLhlCHAIAs4j22ORv4wb1/M2zR2vebCP+HZfZ5+KSiILYZA5oAQ5RmyTlzp3bbQkZByMucejZsyf16tXLqGOMaHL1Ar9GEZsgac4kodwOAMzBqrEp0JkklhAXvTNt0RKbIvjQAayTJA0ePJg++OADateuHdWvX1/ctnLlSpo1axb169dPLJJ96qmn6NatW/T444+T1UXySdnXFuDtqhakmZuP0xPNI7OcEAAil1Vjk79j8hHaPyigmYm6JXMH/VgAwMJJ0pIlS0QHob59+zrcPmrUKJozZ47Yj4Jrw7/66quoCkT+iqIcyWsL8C8frEX9TlyiyoWwwWw4ZcuSma7eTKUy+bOH+1AAQsaqsYlbn/sjXA2EwvFj173Zhs5dvUkl8kbuOVHvFhyRIF+OKF/7BlHD5wLk2bNnU+vWrV1ub9Wqlfga4/0peMdziK5Wl95mkrLEZqKqRZIoUzQNUUagKf2aUNfaReiHnvXCfSgAIWPV2ISZJO8lh7mzZ6HS+XOE6nDAjWEP16ZHGhanrrWKhPtQAIKTJOXJk4emTZvmcjvfxl9jV65coZw5c/r60FHpVmo0pUnR2co82pRPzkmf3V8zokdNAXxl1djk92ayOH9HlGj4dXWoXoje71yNYqO8QQhYuNzuzTffFHXdCxYssNd9r1q1imbMmEEjR44Un8+dO5datGhB0eJ6SqrfC1tvpSo7wEcDvS3AAQBCzWqxKTXNJj6iq17BP1ZoKY14CxABSRLXcleuXJm++eYbmjx5sritQoUKYodzdcO+F154gaLFRzO308hFe2hS30Z+fX+KCGLWKLcDAAgXq8WmDl/9S4fPXaNR3etQJImmtTUAEN382iepSZMm4sMKOEFiH87Ypuv+XzxQkyasOkjL956Nvpkk532SoijYNS+fnxbvPEV3VE4O96EAgJ+sFJu2H78k/r/5yAVd9/+hZ13qPXY1hRsG1/wvowaACEiS0tLSaPfu3XTy5Enxb1nz5s0pGtl8OJH93KcBlX19ZtS1AHcWTU/to67V6O+Nx+jhBsXDfSgA4Ccrxia9xQotKybTy3dWoE9m7Qj2IYGBJvZtRAfOXKXbKxYI96EAWI7PSdLy5cvp4YcfpgMHDri0HuWZhtTUVLIyThx4UWLrSgVo05EL1DqKZiZcyu0oehTOlZUex/5OABHLqrEpzYcFOd0alKAfluyjeiWVRhbRInqK2l3x7yrafl8AUZsk8R4UdevWpb///psKFSpkmQ45euOQOnP03aN16VaajeJC3MUlZ3wsXbpxi7IE4ee6lNdZ41cPABHAurFJf4qQlDWOlg1qRbFh7P/NxwAAEJVJ0q5du2jSpElUtmxZshK9gUiNyxyg4zKHPhD9+kRD+njWdnq5bcUQzCRZ4yIEAMzPqrHJ195AoR64Uw29tzrN2nycHmtWyjQb6gIAeOLz2bJBgwai5hu0hTtt4M1cx/VpQNWKJgX9Z1lkoBYAIoCVYpOcFPhSbhdO99UtRqN71qNsWfxaCg0AEHI+n6369+8v2qgeP36cqlWrRnFxjlPn1atXp2ikNwxFc4kH9kkCALOyUmyS86IIyZEAAKI/SbrnnnvE/3v37u2QGPDIVjQvjtUbiKI4R9LYJymKnywARBQrxSY5HKHUDADAJEnSvn37yIpsOueSorrlN+aOAMCkrBSb5BK7KNqvHAAgspOkEiVKaN7Oe1LMmDHD7dctM5NE0SuaW4ADQGSzUmyS41EqZpIAAIIi4BWUvFD2hx9+oLFjx9KpU6coJSWFohHK7az9XAEgskRzbJIrGyKlcUMw4SUAgGDwqxfotWvX6KeffhI7mFeoUIH+++8/Gjx4MB0+fJiild5AFM3ldheuOV5koPwOAMzEKrEJjRsc7ThxKdyHAABWn0latWoVff/99zRhwgQqU6YMdevWTQSh4cOHU+XKlSmaIRAR7Tpx2fEG5EgAYAJWi01yPErDoiQAgPAmSdw+9eLFi/Twww+L4FOlShVx+6uvvkpWoHekKoonkiz9XAHAnKwYm+TKhqMXroX1WAAAyOrldjt27BAlDLfffntUjswZJZrL7dC4AQDMxoqxSZ47mrHpeBiPBAAgeulOkvbu3StqvJ966ikqWrQovfjii7Ru3TrslePESi8HfvcAEG5WjE3YGwkAwERJUpEiRej1118XHYPGjRsndjVv0qQJ3bp1S3QP2rlzZ3CPNEJYqZmBdZ4pAJiVFWMTliEBAJi0u13Lli3p559/pmPHjtE333xD//zzD1WsWFHUhltdpijOHFzK7aL4uQJA5LFMbEKSBABgniTp6tWrLrclJSXR008/TatXr6a1a9fSbbfdZvTxRR4LJQ5WmjUDAHOyYmzytiVFUta4kB0LAABZPUnKly8f3XXXXfTtt9+KcgZnNWvWpK+++oqsZFyf+pZq3ODMQk8VAEzKirHJ20TSz30ahOhIAACil+4kafv27dS2bVv6/fffqWTJktSgQQP64IMPaNOmTWRVebJncbkNeQMAQOhYMTbJjRvyasShLLF+VdIDAIBE95m0ePHi1L9/f5o3bx6dOHGCBgwYIIJQs2bNqHTp0uJzrv9OTU0lK5ebRXNHJWcWeqoAYFJWjE3eGjfg3AwAEDi/hpu43vuhhx4Su5ufOnWKRo0aJQJQr169KH/+/PTLL7/oepwRI0aIBbWJiYnio1GjRjRz5kz7169fv079+vWjvHnzUo4cOeiee+4RQdAstAJRVDducEoKsSYJAMzEiNgUCXHJJhXcnbly0+XrODMDAAQu4Dn5uLg4atOmDX399dd04MABmj9/PpUvX17X9/KeFh999BGtWbNGLLDlzkR33303bdmyRXz9+eefp2nTptHEiRNp0aJFdPToUeratSuZhdb6IyslDhitBACz8jc2RURcwkwSAEDQxfr6DWlpaZQpUybNGulDhw5RrVq1dD9Wx44dHT7nOnIexVu+fLkIVKNHj6bx48eLIMXGjBlDlSpVEl9v2LAhhZtmIIrm4OTcAjxcxwEAEKTYFAlxCfskAQCYaCbp4sWLdP/991P27NkpOTmZBg8e7FDjffLkSSpVqpTfB8KPxSUSV65cEeUNPIqXkpJCrVu3tt+H97vg+vNly5a5fZwbN26IY5U/gkUrSYjmcjsrr78CAHMKZmwyKi4ZHZvkcjttODcDAIQsSXrzzTdpw4YNYkdzHln76aefRAnCzZs3NTvu6MULbLmuOz4+nvr27Ut//vknVa5cWbRyzZIlC+XKlcvh/hwEtdq8qoYMGSLq0tWPYsWKUbBo5QhWShys80wBwKyCEZuMjktGxyY0bgAAMFGSNGXKFLEI9t5776XHHntM1GrzwlguTeARMn8ThAoVKtD69etpxYoV9NRTT1GPHj1o69at5K9BgwbRhQsX7B9cZhE8WmuSophTYEYgBoBwC0ZsMjouGR2bvCV9ODUDAIQwSeKgU6JECYcN/Ljl6qVLl6h9+/aau57rwaNyZcuWpTp16oiRtho1atCXX35JBQsWFCOB58+fd7g/dxHir7nDI39qVyL1IxCeYqtWaV00byZ7Ky0t3IcAABD02GR0XDI6NnmbGLNSRQMAgCn2Sdq2bZvDbTlz5qQ5c+bQtWvXqEuXLoYtvuXRPw5O3J2IOxKpduzYQQcPHhS14aES42MgiubY1KBUXofPEYgBINxCEZvMFpe8JkmhOhAAgCimO0m64447RBcfZ1y3PXv2bEpISPCr/GDx4sW0f/9+UQPOny9cuJC6desmarb79OlDAwcOpAULFogFs7zXBQeiUHa285QIWC0QPdIwY7S2UqHAZugAAIxgdGyKhLjkrXGD1cavnmhe2v7v4d1qh/VYAMCCLcDfeecdsR+EFh61mzt3Lq1du9anH85dhx599FE6duyYCD68gR8HNd7bgn3++eeipStv1sejeG3btqXhw4dTKHmeSXK9LXMUt7fLEpuRU7/RoVJYjwUAIBixKRLiElqAO8qfI97+7/bVCoX1WADAgklS7ty5xYc7HIxatGjh0w/n/SY84RHAYcOGiY9wUdYYaUckrfVHsVGcJMn8aGQIAGA4o2NTJMQl740brBGHAABMUW6nun79OlmKj7HGKut08uXMEu5DAACwZGzyukuSNcKQXcl82cN9CABg9STp3Llz1KpVK7ISX8vtot3oHnXpvc5VqWJBrEkCAHOwWmzyZ0/CaNa6UgF6rX1FGv94g3AfCgBYsdyO67N5gWyzZs3ISjy19HaeNXquVTmKdq0qJYf7EAAALB2bvLcAd/z8h551KZpxLH6ieZlwHwYAWHEmadeuXdS4cWOqXbt2yBeohpvefZLqlMhNz7cpH5JjAgAA68Ym7+V2GcFp/GMNqGVFDG4BAAQlSeIRurp162q2WbV0uR0WxwIAhI1VY1Oa18YN7j4BAABDk6QrV65QkSJFRNtTq/G4T5L0JdSIAwCEllVjky/ldhjMAwAI4pok3meiQ4cOopXqe++9R1biqdwOoQcAIHysGpu8zyQhOgEAhCRJ4p3EeQdy3jSPdzF/5ZVXyCo8hhrEIQCAsLFqbPJpJglxCgDAL7prFKpUqUJLliyhH374gazEU7mdp853AAAQfFaNTZ7IkQlRCgDAPz4VcpcsWVIEI1Ag+AAAhJ/VYpO3cjs5OFllg3MAAKP5vNo1f/78ZCWeGjIg+AAAmIOVYpP3HCkjNiFMAQD4x1otgQwORog9AABgusYNDt3tAAAgaI0bZGfOnKHBgwfTggUL6OTJk5SWlubw9bNnz1I0QWNvAADzs1Js8rqZrPxvZEkAAKFJkrp37067d++mPn36UHJyctSXnHkutwvpoQAAgBtWik3eu9tF73MHADBtkvTvv/+KBbI1atQgK0jDVBIAgOlZKTZ527wcKRIAQBjWJFWsWJGuXbtGVmFDwR0AgOlZKTZ5LbdDlgQAEPokafjw4fT666/TokWLRA34xYsXHT6sOpOEVAoAIHysFJvSvAQmubudt9I8AAAwqNwuV65cIuC0bNnSZfqf66BTU1MpqiDAAACYnpVik9ewhJkkAIDQJ0ndunWjuLg4Gj9+fNQvjvVWbieP1gEAQPhYKTZ5b9wQqiMBAIhePidJmzdvpnXr1lGFChXICjxVNcTHYZspAAAzsFJsQuMGAIDg8/kqv27dunTo0CGyCnfB6JfHGlBCXOaQHw8AAFg7NnlKkRa+eFtUz6IBAJh2Jql///703HPP0UsvvUTVqlUT5Q2y6tWrkxWCUZOy+UJ8JAAA4I6VYpOniaSS+bLTlRu3Qnk4AABRyeck6YEHHhD/7927t/02HrWKxsWxDJ2BAADMz0qxKc1buR0mkgAAQp8k7du3j6zCW903AACYg6Vik5evo6kQAEAYkqQSJUqQVejdIwkAAMLLWrEJM0kAAKZr3DBkyBD64YcfXG7n2z7++GOKJphJAgCIDFaKTdi/DwDAhEnSqFGjqGLFii63V6lShUaOHEnRBHEIACAyWCs2YSYJAMB0SdLx48epUKFCLrfnz5+fjh07RlYqaZBh0gkAIHwsFZvS9K9JQmgCAAhRklSsWDFaunSpy+18W+HChSmaIPEBAIgMlopNXr6OmSQAgDA0bnj88cdpwIABlJKSQi1bthS3zZ8/n15++WV64YUXKJogSQIAiAxWik1eGzeE7EgAAKKXz0kSb9R35swZevrpp+nmzZvitoSEBHrllVdo0KBBZKW6bwAAMAdLxSYvoYn3hQIAgBAnSXzy5U5Bb775Jm3bto2yZs1K5cqVo/j4eIo2mEkCAIgMVopN3grukCIBAIQwSSpevDh16tRJfHApQ44cOahevXoUzXxp3AAAAKFnzdjk+euYSAIACGHjhnHjxokRuX79+lG+fPnogQceoF9++YXOnz9P0QopEgCAuVkyNrkJToWSEsT/UW4HABDCJKlFixb0v//9j3bt2iW6BdWsWZO+/vprKliwoBi9++KLL2jv3r1khUD059ONQ30oAACgwZKxSWMILzZTDP35dJOwHA8AQDTyuQW4ujkfL4Rdvnw57du3jx588EHRRahq1ari4++//6ZoYNPIksrkz061iucOy/EAAIB7VolNWuV2jzQsQQXTZ5IAACAMjRuc8eZ9TzzxhPi4cuUKzZkzJ2oWymrNJKGMAQDA/KI7NrkGJ4QmAIAwJ0lr166luLg4qlatmvh86tSpNGbMGKpcuTK9/fbb1KVLF4rmxg2IQwAA5mOl2KQ5gIfoBAAQ3nK7J598knbu3Cn+zXXeXM6QLVs2mjhxoti0L5poLUlyN1qHJg8AAOFjrdikfyYJTVoBAEKUJHEQ4oWxjINP8+bNafz48TR27Fj6448/KJpgtA4AIDIgNgEAQFiTJK6FTktLE/+eN28etW/fXvy7WLFidPr0aYomqPsGAIgMVopNWo0bEJsAAMKcJNWtW5fef/99sTfFokWLqEOHDuJ27iSUnJxM0QRVCgAAkcFSsUlzAA9ZEgBAWJMk3nOCF8g+88wz9Prrr1PZsmXF7ZMmTaLGjaNr/yCtxg1Hzl9z+LxwesvVtlWiKwgDAEQSK8UmX9YZFc2dNZiHAgAQtXR3t+OFsKVLl6bq1avTpk2bXL4+dOhQypw5M0V7ILp0/ZbD59P6N6VV+89R60oFQndgAABg3dikUeew++Rlh89nDWhGF66mUOFcSJIAAIKaJHEAKlmyJHXq1Ik6d+5M9evXd/h6QkL0bWKnZ7Aub454urNqwRAcDQAAOLNkbNIITpmcqu0qFkwM2fEAAFi63I4Xvg4ZMoROnjwpghFv1Pf444/TtGnT6Pr16xSN0rRWxwIAgGlYMjZphKbYTD5XzwMAgAe6z6o8GtexY0f6/vvv6dixY6Klat68eemVV16hfPnyiRG8H374gU6dOqX3IQEAAAJixdikVW4XmxmNGwAAjOTX0BN30eGFsB999BFt3bqV1q1bR82aNRP7URQtWpSGDRtG0SBLbCaqXyoPlcmfPdyHAgAAXlglNhXImSBikywTutsBABgqxqbVSzQAZ86cobNnz1K5cuXIDC5evEhJSUl04cIFSkz0r0b76Plr1Pijf+yf7/9IaS0LAADBPf8aJRpj02dzd9JX83eJf3esUZi+fqiWwUcJABB99J5/dTduUP31119uR/C47IEDkFmCkFEwQgcAYG5WjE23UpXNc5nB450AAJbnc5LE9d0cdJxPyOpt/P+mTZvSlClTKHfu3BQNkCMBAJibFWPTLamDA1IkAIAwr0maO3cu1atXT/yfp6n4g//doEEDmj59Oi1evFiUNbz44osULZAkAQCYmxVjUwpmkgAAzDOT9Nxzz9G3337rsIN5q1atRDnDE088QVu2bBE7n/fu3ZuiRQwhSwIAMDMrxqZbqdJMEnIkAIDwziTt2bNHc5ET38Y7nzOu++a9K6KF8yZ9AABgLlaMTbfS5JmksB4KAEDU8TlJqlOnDr300ksOe07wv19++WVR6sB27dpFxYoVo2jBtewAAGBeVoxNKdJMUhqyJACA8Jbb8YZ9vECW95xQg82hQ4eodOnSNHXqVPH55cuX6Y033qBokaq1vTkAAJiG1WMTohQAQJiTpIoVK4pN+ubMmUM7d+4Ut1WoUIHatGlDmTIpE1McqKKJXNIAAADmY8XY5Ni4IayHAgBg7SQpJSWFsmbNSuvXr6c777xTfFhBnuxZwn0IAADghlVj08P1i9P0jcfSP0OWBAAQtjVJcXFxVLx4cUpNTSUriY/NjDbgAAAmZdXY1LhsPvu/URUOABDmxg2vv/46vfbaa3T27NmAf/iQIUPEgtqcOXNSgQIFRCnEjh07HO5z/fp16tevH+XNm5dy5MhB99xzD504cYJCLU82zCYBAJiVUbEpkuKSDPskAQCEOUn65ptvxKZ8hQsXFvXetWvXdvjwxaJFi0SgWb58udj0j0sm7rjjDrpy5Yr9Ps8//zxNmzaNJk6cKO5/9OhR6tq1K4UaZpIAAMzLqNgUSXFJhhQJACDMjRuMXPg6a9Ysh8/Hjh0rRu7WrFlDzZs3Fzumjx49msaPH08tW7YU9xkzZgxVqlRJBLCGDRtS6CBLAgAwK6NiU2TFpQyYSAIACHOS9NZbb1GwcPBhefLkEf/noMSjeK1bt3boYMS158uWLdMMRjdu3BAfqosXLxpybJhJAgAwr2DFJiPiUjBjkwo5EgBAmMvt2Pnz58WeFIMGDbLXf69du5aOHDni94GkpaXRgAEDqEmTJlS1alVx2/HjxylLliyUK1cuh/smJyeLr7mrJ09KSrJ/GLVxIHIkAABzMzo2GRWXghmbVFiTBAAQ5iRp48aNVL58efr444/p008/FUGJTZ48WQQmf3EN+ObNm2nChAkUCD4GHvlTP3gzQSNkwlQSAIBpBSM2GRWXghmbAADAJEnSwIEDqWfPnrRr1y5KSEiw396+fXuxaNYfzzzzDE2fPp0WLFggdktXFSxYkG7evGkPdiruIsRf0xIfH0+JiYkOH0ZAjgQAYF5GxyYj41IwY5MqDTNJAADhTZJWrVpFTz75pMvtRYoU8Vhq4K48gAPRn3/+Sf/88w+VKlXK4et16tQR+1/Mnz/ffhu3Yj148CA1atSIQgk5EgCAeRkVmyIpLsmQIwEAhLlxA4+GaS043blzJ+XPn9/nUgbuEDR16lSxJ4UayLhem3dP5//36dNHjBDyolkeeevfv78IRKHuIIT4AwBgXkbFpkiKSzJUOwAAhHkmqVOnTvTuu++K7j4sJiZGjKC98sorYkM9X4wYMULUZt92221UqFAh+8dvv/1mv8/nn39Od911l3hsbr/K5QxcYx5qKGUAADAvo2JTJMUlGdbNAgAYK8bmY0scDh733nsvrV69mi5duiQ27uORNh5FmzFjBmXPnp3MhEcWeeSPjzuQGvB6H8yjU5eU9q37P+pg4BECAEQno86/elg1NpV89W/x/xbl89OPvesbeIQAANFJ7/nX53I7flDehXzp0qW0YcMGunz5stjNXN4zIhqhvSoAgHlZNTapMmEiCQDAUD4nSSreN4I/rCJHfCydvnwz3IcBAAAeWC02qbLF+x3OAQDA3zVJX331FV2/fp30GjlypCh3iCbDutWmCsk56btH64b7UAAAALFJ+PiealSxYE56vX2lcB8KAID11iRlzpxZ1Hbr7RDE9X3r16+n0qVLk5Vq4gEAIHTnX8QmAAAI65okzqNatWpFsbH6pvOvXbum/0gBAAD8gNgEAADBoiuyvPXWWz496N133y32jwAAAAgWxCYAADBNC/BIg5IGAIDwwPnXPbw2AADmPv/6vJksAAAAAABANEOSBAAAAAAAIEGSBAAAAAAAIEGSBAAAAAAAEEiS5GnjvmPHjvn6cAAAAAFDbAIAgLAmSbVr1xab8Tn7448/qHr16kYdFwAAgG6ITQAAENYk6bbbbqOGDRvSxx9/LD6/cuUK9ezZk7p3706vvfaaoQcHAACgB2ITAAAYSd825ZLhw4dThw4d6LHHHqPp06eLMoYcOXLQypUrqWrVqoYeHAAAgB6ITQAAENYkibVr1466du1KI0aMoNjYWJo2bRqCEAAAhBViEwAAhK3cbs+ePdSoUSMxUjd79mx6+eWXqVOnTuL/KSkphh0YAACAXohNAAAQ1iSpZs2aVKpUKdqwYQO1adOG3n//fVqwYAFNnjyZ6tevb+jBAQAA6IHYBAAAYU2SuO57woQJlCtXLvttjRs3pnXr1onuQgAAAKGG2AQAAEaKsdlsNopiFy9epKSkJLpw4QIlJiaG+3AAACwD51/38NoAAJj7/Otz44affvrJ7ddiYmJEu1UAAIBQQmwCAICwziTlzp3b4XNeEHv16lXKkiULZcuWjc6ePUtmgtE6AIDoP/8iNgEAgJHnX5/XJJ07d87h4/Lly7Rjxw5q2rQp/frrr74+HAAAQMAQmwAAwEg+J0laypUrRx999BE999xzRjwcAABAwBCbAAAgrEkS4437jh49atTDAQAABAyxCQAA/OFz44a//vrL4XNe0nTs2DH65ptvqEmTJn4dBAAAQCAQmwAAIKxJUufOnV26BuXPn59atmxJ//vf/4w8NgAAAF0QmwAAIKxJUlpamqEHAAAAECjEJgAAMOWaJAAAAAAAAMvMJA0cOFD3A3722WeBHA8AAIAuiE0AABDWJGndunW6HoxrwAEAAEIBsQkAAMKaJC1YsCBoBwAAAOAPxCYAAAj7mqS9e/eKlqoAAABmgdgEAABhTZJ45/JTp07ZP3/ggQfoxIkTQTkoAAAAPRCbAAAgrEmS80jdjBkz6MqVK8E4JgAAAF0QmwAAIBjQAhwAAAAAAMCfJIm7Azl3CELHIAAACCfEJgAACFt3O7WkoWfPnhQfHy8+v379OvXt25eyZ8/ucL/Jkycbf5QAAAAaEJsAACCsSVKPHj0cPn/kkUeCcTwAAAC6ITYBAEBYk6QxY8YE5QAAAAD8hdgEAADBgMYNAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAEiRJAAAAAAAAZkmSFi9eTB07dqTChQtTTEwMTZkyxeHrNpuNBg8eTIUKFaKsWbNS69atadeuXWE7XgAAiH6ITQAAENYk6cqVK1SjRg0aNmyY5tc/+eQT+uqrr2jkyJG0YsUKyp49O7Vt25auX78e8mMFAABrQGwCAIDYcP7wdu3aiQ8tPFL3xRdf0BtvvEF33323uO2nn36i5ORkMar34IMPan7fjRs3xIfq4sWLQTp6AACIRohNAABg2jVJ+/bto+PHj4syBlVSUhI1aNCAli1b5vb7hgwZIu6nfhQrVixERwwAANEOsQkAwBpMmyRxEGI8Oifjz9WvaRk0aBBduHDB/nHo0KGgHysAAFgDYhMAgDWEtdwuGOLj48UHAACAWSA2AQBEFtPOJBUsWFD8/8SJEw638+fq1wAAAEIJsQkAwBpMmySVKlVKBJz58+c7LHTlTkKNGjUK67EBAIA1ITYBAFhDWMvtLl++TLt373ZYELt+/XrKkycPFS9enAYMGEDvv/8+lStXTgSmN998U+xb0blz53AeNgAARDHEJgAACGuStHr1arr99tvtnw8cOFD8v0ePHjR27Fh6+eWXxX4VTzzxBJ0/f56aNm1Ks2bNooSEhDAeNQAARDPEJgAAiLHxpg9RjMsguN0qdxNKTEwM9+EAAFgGzr/u4bUBADD3+de0a5IAAAAAAADCAUkSAAAAAACABEkSAAAAAACABEkSAAAAAACABEkSAAAAAACABEkSAAAAAACABEkSAAAAAACABEkSAAAAAACABEkSAAAAAACABEkSAAAAAACABEkSAAAAAACABEkShMauuUSbJoX7KAAAABRpaUSrRhMdWhXuIwEAE4oN9wFAFLp6lujGJaLd84iq3UdENqJf7lW+lr8iUcGqGfc9tYNozY9EzQYSZc9n3DHw4yYVJcqSnej6BeWYciQTzR6k/H//EqJz+4kaPUPU6OkAfs5OokMriGo+TJQps3HHDwAAxrp4lOjySaLTu4iq3Uu0fRrR3wOJsuYmenkfUUxMxn23zyA6uZWo6fPGndtTbxGd2kaUXFX5WReOEMXGE10+QbR8OFHWPER7FhCl3SLq8D+ikk38/1kcf1nZ1sYcO4AFIUmKZjtnE53cRtT4WaJMmYhuXlVOxnlKBe9nplwj+kR6/I2/EXX4LOPzkU2Inl5BVKCiErD+foFo/79ExzYQ9frb9fE4kRn/IFHDp4jq9FBu2zGL6MBSotteVZIgxsHm7B6i4o2JTm4hGtXc9bHylFHuI+OkqXhDovicRPnKuX4PJ1jrxxNV6kiUvQBRbBaiSyeIPq+sBDI7G1H1B5XnUqoF0Y2LRHHZiOISfHwBISRsNqITW5T3XVoKUenbiXKXCPdRAVjjb48TgqRiRJU7KbfxOTVzHFG2PMH7uVumEE1MjyGMY+Lp3cq/r50j+q4lUe/ZRDGZiK6eIZrwkPI1jpdV73F9vM1/EC3+lKjrd8rAHz+vpV8QJeQiqt1DeXx2eLWShOUqQbR8GNHcwfqOlwcWH/yFqHBtoqy5tAfo9i1SBugyxxNljlVmxEZLSRE/l2fXE2XOosTbIrWJLh0nylnQMSEE80hNITq4jOjMbqKEJCXJ5f9DWMTYbPyXHb0uXrxISUlJdOHCBUpMTKSocmaPcpIuf4dywuQRL76IV73NJ1abchLnC/29C5Tbn1xMVKiGscdy8RjRgg+IClRWEg9ZlpxENy853tZ5BNGUpxxv6zWTKE9pZTRt3TiihR8RXTmZ8fVXDyrB4IPkjNs4eNXtQzSxZ8Z9+YTCyY2vXtxFlCWHkkht/1spDzyzy/E+jy9QjvvUdu+PV6E90UO/kqHO7iXaPJmoVneinNLrAJ5xQnR0PZEtVZll5AEE599t9QeUhD4+R7iOMupE9fk3QFH92vBA1s6ZRCWbEaXeVC7kVQeWEY25U/n3o38RzX6d6MQmouz5iZ7fosysGGnfYuV8vneRMovjTcN+SjIje36rcu7gxO6PPkTbphOl3lC+xjHjuY3KuVlOUFoNJspdimhSr8COP29ZomdWK9UQp3cQrR5DdGSN44BfYhGih39XBiH1aP8pUf3HyVB7FyrXIXV7KQkv6JtZ5AHf8weVvxNOqHfOIrp2NuM+fM3T+m1loBiJbcjPv0iSQnIQR4nW/UJUpydRjvzayQ5fkDfsq1zgb/xdObG3+4QoSzaiWzeIpj5DlFyFqHF//rUp3/dNHeXErLr9daIWLyv/5l/rOxqjT6qq9xJ1GaWMPnFdNgeP2ARlhI9nRIrV0//8lnxONO9tCgk+YajByWh8kcwzX0bi15gDGv8OLx0javWWY7khz7wt/YqoRGNldI8TxJQrynuGZ6X4YqNAJWVU6fpFZZZMxiOfPBOWlqo81uFVRIVraY88WsnNK0T/fqaUVV4+rsxIar2XijdQAtWh5US2NKJ8FYgeGEeUvwKZ1o3LREfXKSPCfE5IrkxmZYrzr0mZ4rU5uILoyGqihk9rX4BxydbZfcoFNceUOW8o5ykRh9Jj1/QBRC1eJSrWQClL47+jd51mhAZsIspVXPk3JywTpKTJ2Z0fKReE6t/x+UNEV04RbZpI1OYdZVZGD45rnLTw+TfScTXIf18Z+5icoG6epMSNTLFK4iQPEPEsG3+9bBtlNo1/F3zfWxyzvlQ+Z9fOE53YTHTzcsb35ihI9MRC5b3CFRc8U8cDVaWaoyydr9kWDVUS3nMHiK6edr1PtnxEResq9z29U7mt8t1Enb4hSjDxefTySeXvjd8bpW8zdgmFwZAkmSkQjelAdGCJMkvy7DrXrw8tlzELMmAz0RfVlBkgNVhsm0b02yMZ9y/WUJkp0RoVq9KV6K7PlYvCb1t4Pi51RmnBh0SLPs64PTYr0b2jia6cJsrBJWbxRGVaun4/n/h+f1RJ6CINv0ZXzhAteN+/789fSTnJ8QWBL/hCoskA5QTIwX5YA9cSwECVb0f08ATlRLXv34ykixOxInUoLPg9zCUsPFAQbBy0x7RT1hOouNyEk0kugeT3NJfXiTKGxIzRbZ6N5IQqLjtRyzeIGjxproDOF6s8c7xxglTqGUN031iiKp3JjExx/jUpU7w2b6eX8dz1hTID4O7rFe8iav4i0be3KZ+/dkwZwBvXhWjPP8ptfKEtBpp+V0pYnd32mvIY/7ynDKx5PK70SoARTZQLcFX9J5S1plyCxrP5HJfylnH9/v1LiX66W/s4zCypONH9Y4kmP+k6060XJ7w82OkrPufxmuFybZWERy6bNwoP/PJ5lWdOjm1Ukq4LB4lqPao9gBxsPEDGJZKcvBWrH/yfx+WQP99DdEOqdOGBUY7L/PfDJf8cl0o0UQaw+fJ85XdEs19T3su5Syp/q2VuJ9Ow2ZTByIVDlFkxOdF7bF5wl3cEAEmSGQMRe/0E0cUjRF/XVj7nPwb5jSXjPxw+YfEFJpcjBAOftGamzz558uYZZcTj1nXlDzXlupIg7ZpNpsUJ6Ve13AdgLg/gQOoPLqWo04toytPKSP6//yNT6TUro6RFxo0qeGaK13DxBUbhmq6lEXxKuHBIOcnxhZAnl08p6wjUZIJHH49vUMouKt2ljB5zMPzxLuXrj0wmKtvKqGep/G0sH0l012dKGSKvLeNjmfWqsoZMlLyUICpYzfsINI+C/fGYUufPCtUk6vSV8aWp7vDrzmsGeYYo5aoyW8SLxxmP3h5ZqwyesMSiyr/5XMKKps/88rrD6+eVAQxOpHhGjAdO6j0WlhFIU5x/Tcp0semt88o6Gy4nYzUfIVr/s/b3dfmW6PBKolXf+146xrPievSZ51i+poX/DgZuITq5PX0mPpcygPhR+qxVsOUrnzHS74t+q4iGOVVrtHmXqMlzyr/H3qVUEviDZ3D4PLDsG+U1Wf0DmUrHr4imPet6Oy8X4HJG/v3VeFBpsOQ8SMUVEzwAzNcgngawxLn0pGNJOg8Y8jUMr9Ou/6RSkcIzYitGOib+RuCfv+gT5Rze4VOiee8Q1e2tDA7wNVPR+kSNnyHKWUiJM/IyCS1chseDeByXGQ9GtP0wdDM1qbeUWMMzujz4yjPMB/5TZh55NuzcvvQ7xijXFzygxzGLq5OK1FX+zRUQHJf498u/Ox60rPGQUtWkrt8LISRJZghEG34j+vMJx9v4YoZnE/gEFkke+k05sfEbnZMPXpezZ35ofnbfJUQTe/k+sjbosDIbxskQl4TIQZXJwZQX2/LFperp5cofO+PHWDZMCcB8Ui1/J1G7j5ULcpU8ohqJeIQ2PlF5jlxSo2r5JtE/PNtmU0pBeX0brzvj23hGw1ectPBaLb640DtLw6coLuvh9x+PtHHys/gT9/fnNQI8Esp13Bx4fcElOmt/JJr7ljLaxwuf+TXg7ot6j5Vry3nmisuX+PF4pDRbXsf3i4ov7rhEkoPflj+9X3CVaUV0+2vKTCRfMPBCdE4UveH1HjyDWbyRMrPIwY6PiZuM8N8BX3hwMmhwzbspEgGTCutr88v9rgNcfNHFo9b2C54w42Y5et7bD/ysVFrwhedDE4iGlqaQ4TJCUfnhIx6o49lrrgaZnn6O4nNr9fuVf/MaYuc1u4yvHbhMTm0IdHiN8nvkUmxOhu78UBkQcfhZEbzon6+XOJGo0kVJ4LdPz2jCxK8Vz14wrgzg8z2fd/96xr8Syyf/VSoN8pXV/z18vt86RSmZ58SF/81NF9zheMLVJ08sUgYofcG/Y467K79V4jEP+t33I1FpLxVDKo4X/KEmZLwEgCtMeGZWa6CUk/TTu5QZW14OIq+T0ipdr/1oeqJbRFmjztUces4lHHca9VdmhPln8Nq7gtWV5l5qmTwnxQZDkmSGQBTJJydP+GQ+2eBFn55wwwa+QP+mnutFJI8IrRyl/HvQEaL576SfRKQZIzZrkFKC8OCvRBXbZ9zOoyF80vqrf8b6rp5/E5Vs6tsxct08T4nzjAU3sDAKXxTrTUbv/0mZ3YsEXBJyZ3qAk3GXKy6HUy/Wf+yUMbPjKx6J9mVtnfNx8GzUlsnK5zzaxyNofGyVubTNpgSWmMzKWonjm5TSEXW9HAdbXkzNJYZqcOHH4GSLZ9c4MeGZr7U/ZcwOqUGUExpOqHgklX8WjxbyrBC/JxMLu47w7ZqjJIWim2JWJXiKNWkxSqDjAO5ugIG/h5M6fnwe6Ww3VCnzMAiSJBO+Nt7Wq0aypgOJlkjdVINJHnDTivW8vpFLssXXLzjeR41N/LsY3lC5qOy/Wjm/qLfzhSmXoKnVDpwAvHnK9+PcPZ9ozRilaxo3BTCKVrdYLXzcD47P2AbE7LpNIirXxvE2Ti74XK7+fvhc/0FB/3/G4LP+l3JzYjztuYzqIi4VPLtfmZnhkno+Nh4A44E3TiwvHFZuU0tPeUCYZxh5tofjVaY4ovJtiRr1U35XPAPHg/jOFU6Z49OXXyQoCUzFjkrs4ljFx+A8CMizRrw/Jsc07kLMsYarTvhajgfbeQCEfw53AnbG38M/jyuXOM7yWqwaD5CRkCSFMxDxm4xLld7VucA0ENw5rvNw5cJcT8e1cOFaZ73H9/BEZe8h/gPmkg/+g2SfllfKkWRcvvhNXeVCkssMuGvZ+PuVdVVvHM+4H7/NeYQie17tnzmsYcYar8f/CWztji/JMbeKbT9UaQvLHZN4dInr/6f0zShP4xIVvpDlWRQ+8XAJIe+z0WNaxsWOut6NOzj9lN5WV8YzBv4mG8GQt5yy3o4Dd82HiI5vJhrb3rGcx3kW1ldvng68y9L894j+/TSwx+Bac4d28U645JZHyjjIcVJkdFncrZvK7BgHJf4b5CDFgfLoWtfjSq6mjODzsXA3L7444AW4fr6OSJJM9NpwQs2z5ZxQfxmCElJeO1u7O9HUfmRqPHhxbL2++/JeSjzTzlUGNR7OiCfO5/ya3ZQZndF3EDV5VplBn/mKUtrFHVnvlUrgxAVsqnZXTU6ePkuPf/x3ywOG/uIBQR7d1+uRP4hylVS61fIFNc9YcDncvLeUi+lXDykxk9dR8evAJVi/3KesbeJ48316WXXbIcpehL8+RLQjvXzYn2QrVPh3x2XKPMjFvyseXOUBUMZVJHy8zt0PfcEdD5/T+X5zh2eBfupMdPC/4MUmvoYqzb/zoso6Z14HZfQaXS7X50SJOy+L62b+e4rRXl7CS084eeVrHx545EQvgOs0JEnBCEQ89ce1ofyHwyPdO+coNa88XajukcCLVuUF477gPX58edPzFHPXbzNGN/hi6P0wLH7Ug/dq+Epjevm1o0R/PqmMMvACRh794ClkrdHsSX2Ubjus+xTldeeRCT5h8Ii+2qmP24fzRZ4vHcpGNiM6vtG11M4fi4cSbf0r4/G46yDvT8EJwa8POt63Uielm5rWAk/uOsUzZc71uhxUeZSFb1eDM89gcVmic8Dmi28upeNRIh6BSiyktMLmPUD4hHTnx0ST08sz+PXkfYN4UTSXGHKZGK9t4hMRJ55qC3m5jTu/zitGKd/D9d68F8hv3ZSv955D9MMdrv82CpdHcFkFnzR57QIfA1/4O3d6DAS/n2a8QLRrntLRi58jv348Y8MzS3zRyaNwHEQ4ueFRNk4oOCHnETweQePSAb4fv04buCV8TPpMUX6iZi8oSUg48FqxOa8riRGX7/HFLK+FcsbPjVvZ+1GKhyQpRK8NL5zmc2CJRsr5kEs3ec0hxwYeteXuo6u+o6Div0O1yULnkUqJlFoWtmGCcp43o05fK5UEzrgkii+OeSE9Nyfi0XJ3Zbcfl1Iu3HiNBW+gzkkCxyM+R6h7+fEF4bapyvWD3g593FxILR/kmeUX/Vj/pOJEjOMPzzyrHvtHiSNcKj7/Xcf7u9sqhJcRcMJWQWPNq/p8eU2quuZKNJbposSf76UGUDywx91f+by9b6EyWMTl7FxCxzGHZ875czW+8ew+D5hyR0ZeO8SDgjxI/J7GwCfvxcjnap5JESXF5zPa0qulpfy75dJvvu5wt/bOH/x3x9uS8Huft5vgnyPHpv5rtZuN+IrfGxyXGTfD4CSYO+jytRQPJnPDLX6v8WAFHwsnPXwO5zWvl44qsZoTPk50eU01v8f5e7nsngdkuXwxd5j2DuTraK4I4kFkroJQ3wfOuPKhgX+DqUiSghGI1NkG7jrHCcrwBhlT57xIL5Ayq5yFibr/mfGYesitUsNd4sejZuoiXrFxXnFl7Yq4KEzfg4gbRHBXG25bLMqMnErivOGLUz7h8IWlkYv/2be3Z5zEOKELtCOLXNLCMz4cYBkvcuQkmmfKeJ0Tz37x6+Ev9ffNJ8I+sx1v4ynrt865Pz4OmhzIef0N1x7fN8b9HiV80c8nUi7J8nSC5wRudBui+CSintPTG0DkVQInl4+oi8J9waOnnHzwbBoHOk7q2rynjNA6j5SP66yMdnX7w9CyMUvgixC+iOARYV7czK83/706t3D2AZKkELw2nBS9nz5Q9sJOZbaCS844aXlqaeAxgZOIQyuVwSdP+EKYkzN1xkXeGJYHGX7R2JA1FDheqI11+KKQL/B5MIhnStkr+5WNZPmCWV4r7Ets4njGF+ScHPJAlFF4HcpHxTLaar+YXr4XCH4t1IRIfo6cfPDMAg8K3bhE9NR//u9ZxRfi/6vguE0Fl7OrjZRuG6RsBu8uNnEJPL+vea0Zr6Ns8ZL7n8UDinzeavGK5+Plkmgu3+ZrpuYvKQ1E+P3AOKbw789Xg88pA0s8MCb2z5qmXYnCz/3Hu5V1O56eC2g7sVX5HfP6cp6s4OUNXOrHVTb91wT1/IurCF+o5Vi8VkHGF26BJEjl7lA2gtMaqeU9i9QF8jxqwtP0vBiOR+aqpY8iGN2g4Vc/aj95ZF1eXPrwb0rAVJMk/jqPKDEeXeGgwrMovuBStF4a0/VGUBMkxqMpgeLfJe9XxH/Q8gmTR2b4g6ftb3/DuAt5nnr29fjUn817j3jDFzxtdbzH+feszmgx+cK62r3KhzojyGtvuIGAJ9zuVN1tnANg9fuUx9D6W+Hnw4kZ+IdnxblFu9ymfWhZ5XfEF2tgTvLs3//KZ/ybZ3V4e4dAqAvMeRDGOUniMiuebRb/LkZ0B58fYpQBDeeZkpI6Nzn1hGcd/JmNEiU8lDHizh+fV83oFMbHqm7NwTPvM15U1kD4gi9++cNoXNam4u0JjMBlgguGKNcdMt4zTp1d4gvQQEqreDZCpb4X+H2h8pTM8LmdZ0V5tqh7+ppQT3jdqZ61pzwb9YrUSIBnS1R8XcX7RPEAHydPvMWHt42HeeCXZ+HUMsn7x7mfbedZs+eD1KHYCpIrKx884MG4PJ83Tua1vUGGJMkI/iQVrd9R6it5RqF2T/d/XHcPU8qG+I+MR1jU0is+0fuq30plEz9PLVidW1HyReq6n5UT6kIPAZf3llHxaJQ6k6GVeHAZHI/e8WyDGckJXyC4cQDX+6olJ+6SlEDwiZ3LuO54jyIG78HV8UtldolnKnjNT4mmSlLJF+o8as0BlRdtcqmLM+w6HjrcKVBt+wrmxGW87sj73+nFpcw8I8wXsmoHLq3OjE/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", 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" ] @@ -611,7 +599,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -660,7 +648,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rubix", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -674,7 +662,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.10" } }, "nbformat": 4, From eb75420962eac8567093f4fe40427501dafd7186 Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 3 Jun 2025 10:08:02 +0200 Subject: [PATCH 32/76] notebook changes --- ...ine_single_function_shard_map_memory.ipynb | 148 +++++++++--------- 1 file changed, 74 insertions(+), 74 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index 93ea83b0..3a92cff5 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -11,7 +11,7 @@ "\n", "# if we're running on CPU, need to pre-specify # cores for explicit parallelism\n", "# used to have to do import os; os.environ[\"XLA_FLAGS\"] = \"--xla_force_host_platform_device_count=8\"\n", - "config.update('jax_num_cpu_devices', 16)" + "config.update('jax_num_cpu_devices', 32)" ] }, { @@ -23,7 +23,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15)]\n" + "[CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)]\n" ] } ], @@ -109,23 +109,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-06-03 09:48:00,970 - rubix - INFO - \n", + "2025-06-03 10:06:31,423 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-06-03 09:48:00,972 - rubix - INFO - Rubix version: 0.0.post437+g49a6496.d20250603\n", - "2025-06-03 09:48:00,972 - rubix - INFO - JAX version: 0.6.0\n", - "2025-06-03 09:48:00,972 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15)] devices\n" + "2025-06-03 10:06:31,424 - rubix - INFO - Rubix version: 0.0.post437+g49a6496.d20250603\n", + "2025-06-03 10:06:31,425 - rubix - INFO - JAX version: 0.6.0\n", + "2025-06-03 10:06:31,425 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)] devices\n" ] } ], @@ -153,7 +153,7 @@ " \"snapshot\": \"1024\",\n", " },\n", " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": False, \"subset_size\": 2000},\n", + " \"subset\": {\"use_subset\": True, \"subset_size\": 100},\n", " },\n", " \"simulation\": {\n", " \"name\": \"NIHAO\",\n", @@ -180,7 +180,7 @@ " \n", " \"ssp\": {\n", " \"template\": {\n", - " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " \"name\": \"FSPS\" #\"Mastar_CB19_SLOG_1_5\"\n", " },\n", " \"dust\": {\n", " \"extinction_model\": \"Cardelli89\",\n", @@ -376,44 +376,44 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-06-03 09:48:02,133 - rubix - INFO - Getting rubix data...\n", - "2025-06-03 09:48:02,134 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-03 09:48:02,193 - rubix - INFO - Centering stars particles\n", - "2025-06-03 09:48:03,031 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-06-03 09:48:03,034 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-06-03 09:48:03,035 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-03 09:48:03,036 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-03 09:48:03,037 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-03 09:48:03,039 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 10:06:31,774 - rubix - INFO - Getting rubix data...\n", + "2025-06-03 10:06:31,775 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-03 10:06:31,816 - rubix - INFO - Centering stars particles\n", + "2025-06-03 10:06:32,689 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100 for stars\n", + "2025-06-03 10:06:32,691 - rubix - INFO - Data loaded with 100 star particles and 0 gas particles.\n", + "2025-06-03 10:06:32,692 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-03 10:06:32,692 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-03 10:06:32,693 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-03 10:06:32,696 - rubix - INFO - Calculating spatial bin edges...\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:03,063 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 09:48:03,237 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-03 09:48:03,246 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 09:48:03,706 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-03 10:06:32,718 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:06:32,890 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 10:06:32,899 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:06:32,919 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:04,191 - rubix - DEBUG - SSP Wave: (5333,)\n", + "2025-06-03 10:06:32,975 - rubix - DEBUG - SSP Wave: (5994,)\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:04,202 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:06:32,990 - rubix - INFO - Getting cosmology...\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:04,685 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-03 10:06:33,039 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:05,306 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-03 09:48:05,307 - rubix - INFO - Compiling the expressions...\n", - "2025-06-03 09:48:05,308 - rubix - INFO - Number of devices: 16\n", - "2025-06-03 09:48:05,457 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-03 09:48:05,569 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-03 09:48:05,574 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-03 09:48:05,601 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-06-03 09:48:05,871 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-06-03 09:48:05,872 - rubix - INFO - Convolving with PSF...\n", - "2025-06-03 09:48:05,875 - rubix - INFO - Convolving with LSF...\n", - "2025-06-03 09:48:05,880 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-03 09:48:08,231 - rubix - INFO - Pipeline run completed in 5.20 seconds.\n" + "2025-06-03 10:06:33,227 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-03 10:06:33,228 - rubix - INFO - Compiling the expressions...\n", + "2025-06-03 10:06:33,229 - rubix - INFO - Number of devices: 32\n", + "2025-06-03 10:06:33,421 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-03 10:06:33,529 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-03 10:06:33,534 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-03 10:06:33,561 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-06-03 10:06:33,798 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-06-03 10:06:33,799 - rubix - INFO - Convolving with PSF...\n", + "2025-06-03 10:06:33,802 - rubix - INFO - Convolving with LSF...\n", + "2025-06-03 10:06:33,807 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-03 10:06:42,423 - rubix - INFO - Pipeline run completed in 9.73 seconds.\n" ] } ], @@ -435,51 +435,51 @@ "text": [ "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:08,865 - rubix - INFO - Getting rubix data...\n", - "2025-06-03 09:48:08,866 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-03 09:48:08,893 - rubix - INFO - Centering stars particles\n", - "2025-06-03 09:48:09,396 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-06-03 09:48:09,397 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-06-03 09:48:09,398 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-03 09:48:09,398 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-03 09:48:09,399 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-03 09:48:09,400 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 10:06:42,625 - rubix - INFO - Getting rubix data...\n", + "2025-06-03 10:06:42,625 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-03 10:06:42,658 - rubix - INFO - Centering stars particles\n", + "2025-06-03 10:06:43,177 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100 for stars\n", + "2025-06-03 10:06:43,178 - rubix - INFO - Data loaded with 100 star particles and 0 gas particles.\n", + "2025-06-03 10:06:43,178 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-03 10:06:43,179 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-03 10:06:43,180 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-03 10:06:43,181 - rubix - INFO - Calculating spatial bin edges...\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:09,410 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 09:48:09,419 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-03 09:48:09,428 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 09:48:09,898 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-03 10:06:43,192 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:06:43,201 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 10:06:43,210 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:06:43,242 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:10,376 - rubix - DEBUG - SSP Wave: (5333,)\n", + "2025-06-03 10:06:43,288 - rubix - DEBUG - SSP Wave: (5994,)\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:10,389 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:06:43,299 - rubix - INFO - Getting cosmology...\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:10,877 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-03 10:06:43,348 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 09:48:11,356 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-03 09:48:11,357 - rubix - INFO - Compiling the expressions...\n", - "2025-06-03 09:48:11,358 - rubix - INFO - Number of devices: 16\n", - "2025-06-03 09:48:11,454 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-03 09:48:11,542 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-03 09:48:11,546 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-03 09:48:11,548 - rubix - INFO - Calculating IFU cube...\n", - "2025-06-03 09:48:11,549 - rubix - DEBUG - Input shapes: Metallicity: 125, Age: 125\n", - "2025-06-03 09:48:11,678 - rubix - DEBUG - Calculation Finished! Spectra shape: (125, 5333)\n", - "2025-06-03 09:48:11,679 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-06-03 09:48:11,683 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-06-03 09:48:11,684 - rubix - DEBUG - Doppler Shifted SSP Wave: (125, 5333)\n", - "2025-06-03 09:48:11,684 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-06-03 09:48:11,719 - rubix - INFO - Calculating Data Cube...\n", - "2025-06-03 09:48:11,723 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-06-03 09:48:11,724 - rubix - INFO - Convolving with PSF...\n", - "2025-06-03 09:48:11,727 - rubix - INFO - Convolving with LSF...\n", - "2025-06-03 09:48:11,730 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-03 09:48:14,080 - rubix - INFO - Pipeline run completed in 4.68 seconds.\n" + "2025-06-03 10:06:43,393 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-03 10:06:43,393 - rubix - INFO - Compiling the expressions...\n", + "2025-06-03 10:06:43,394 - rubix - INFO - Number of devices: 32\n", + "2025-06-03 10:06:43,495 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-03 10:06:43,577 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-03 10:06:43,580 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-03 10:06:43,582 - rubix - INFO - Calculating IFU cube...\n", + "2025-06-03 10:06:43,582 - rubix - DEBUG - Input shapes: Metallicity: 4, Age: 4\n", + "2025-06-03 10:06:43,708 - rubix - DEBUG - Calculation Finished! Spectra shape: (4, 5994)\n", + "2025-06-03 10:06:43,709 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-06-03 10:06:43,714 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-06-03 10:06:43,714 - rubix - DEBUG - Doppler Shifted SSP Wave: (4, 5994)\n", + "2025-06-03 10:06:43,715 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-06-03 10:06:43,752 - rubix - INFO - Calculating Data Cube...\n", + "2025-06-03 10:06:43,755 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-06-03 10:06:43,755 - rubix - INFO - Convolving with PSF...\n", + "2025-06-03 10:06:43,761 - rubix - INFO - Convolving with LSF...\n", + "2025-06-03 10:06:43,765 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-03 10:06:52,006 - rubix - INFO - Pipeline run completed in 8.83 seconds.\n" ] } ], @@ -550,7 +550,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -599,7 +599,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] From 61d61562d4a88250777be088d88c4f4d3faf544a Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 3 Jun 2025 10:17:50 +0200 Subject: [PATCH 33/76] notebooks --- ...ine_single_function_shard_map_memory.ipynb | 140 +++++++++--------- 1 file changed, 69 insertions(+), 71 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index 3a92cff5..e6368d29 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -116,16 +116,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-06-03 10:06:31,423 - rubix - INFO - \n", + "2025-06-03 10:12:03,586 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-06-03 10:06:31,424 - rubix - INFO - Rubix version: 0.0.post437+g49a6496.d20250603\n", - "2025-06-03 10:06:31,425 - rubix - INFO - JAX version: 0.6.0\n", - "2025-06-03 10:06:31,425 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)] devices\n" + "2025-06-03 10:12:03,587 - rubix - INFO - Rubix version: 0.0.post438+gd14bd2b.d20250603\n", + "2025-06-03 10:12:03,588 - rubix - INFO - JAX version: 0.6.0\n", + "2025-06-03 10:12:03,588 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)] devices\n" ] } ], @@ -153,7 +153,7 @@ " \"snapshot\": \"1024\",\n", " },\n", " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": True, \"subset_size\": 100},\n", + " \"subset\": {\"use_subset\": False, \"subset_size\": 100},\n", " },\n", " \"simulation\": {\n", " \"name\": \"NIHAO\",\n", @@ -180,7 +180,7 @@ " \n", " \"ssp\": {\n", " \"template\": {\n", - " \"name\": \"FSPS\" #\"Mastar_CB19_SLOG_1_5\"\n", + " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", " },\n", " \"dust\": {\n", " \"extinction_model\": \"Cardelli89\",\n", @@ -376,44 +376,43 @@ "name": "stderr", "output_type": "stream", "text": [ - "2025-06-03 10:06:31,774 - rubix - INFO - Getting rubix data...\n", - "2025-06-03 10:06:31,775 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-03 10:06:31,816 - rubix - INFO - Centering stars particles\n", - "2025-06-03 10:06:32,689 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100 for stars\n", - "2025-06-03 10:06:32,691 - rubix - INFO - Data loaded with 100 star particles and 0 gas particles.\n", - "2025-06-03 10:06:32,692 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-03 10:06:32,692 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-03 10:06:32,693 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-03 10:06:32,696 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 10:12:04,695 - rubix - INFO - Getting rubix data...\n", + "2025-06-03 10:12:04,696 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-03 10:12:04,761 - rubix - INFO - Centering stars particles\n", + "2025-06-03 10:12:05,095 - rubix - INFO - Data loaded with 739749 star particles and 0 gas particles.\n", + "2025-06-03 10:12:05,096 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-03 10:12:05,096 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-03 10:12:05,097 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-03 10:12:05,099 - rubix - INFO - Calculating spatial bin edges...\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:32,718 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 10:06:32,890 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-03 10:06:32,899 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 10:06:32,919 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-03 10:12:05,569 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:12:05,711 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 10:12:05,720 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:12:06,180 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:32,975 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-06-03 10:12:06,666 - rubix - DEBUG - SSP Wave: (5333,)\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:32,990 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:12:06,678 - rubix - INFO - Getting cosmology...\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:33,039 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-03 10:12:07,161 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:33,227 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-03 10:06:33,228 - rubix - INFO - Compiling the expressions...\n", - "2025-06-03 10:06:33,229 - rubix - INFO - Number of devices: 32\n", - "2025-06-03 10:06:33,421 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-03 10:06:33,529 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-03 10:06:33,534 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-03 10:06:33,561 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-06-03 10:06:33,798 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-06-03 10:06:33,799 - rubix - INFO - Convolving with PSF...\n", - "2025-06-03 10:06:33,802 - rubix - INFO - Convolving with LSF...\n", - "2025-06-03 10:06:33,807 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-03 10:06:42,423 - rubix - INFO - Pipeline run completed in 9.73 seconds.\n" + "2025-06-03 10:12:07,782 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-03 10:12:07,783 - rubix - INFO - Compiling the expressions...\n", + "2025-06-03 10:12:07,784 - rubix - INFO - Number of devices: 32\n", + "2025-06-03 10:12:08,012 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-03 10:12:08,123 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-03 10:12:08,128 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-03 10:12:08,154 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-06-03 10:12:08,384 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-06-03 10:12:08,385 - rubix - INFO - Convolving with PSF...\n", + "2025-06-03 10:12:08,388 - rubix - INFO - Convolving with LSF...\n", + "2025-06-03 10:12:08,393 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-03 10:12:10,922 - rubix - INFO - Pipeline run completed in 5.83 seconds.\n" ] } ], @@ -435,51 +434,50 @@ "text": [ "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:42,625 - rubix - INFO - Getting rubix data...\n", - "2025-06-03 10:06:42,625 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-03 10:06:42,658 - rubix - INFO - Centering stars particles\n", - "2025-06-03 10:06:43,177 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100 for stars\n", - "2025-06-03 10:06:43,178 - rubix - INFO - Data loaded with 100 star particles and 0 gas particles.\n", - "2025-06-03 10:06:43,178 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-03 10:06:43,179 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-03 10:06:43,180 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-03 10:06:43,181 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 10:12:11,611 - rubix - INFO - Getting rubix data...\n", + "2025-06-03 10:12:11,613 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-03 10:12:11,717 - rubix - INFO - Centering stars particles\n", + "2025-06-03 10:12:11,775 - rubix - INFO - Data loaded with 739749 star particles and 0 gas particles.\n", + "2025-06-03 10:12:11,776 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-03 10:12:11,777 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-03 10:12:11,778 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-03 10:12:11,781 - rubix - INFO - Calculating spatial bin edges...\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:43,192 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 10:06:43,201 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-03 10:06:43,210 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 10:06:43,242 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-03 10:12:12,363 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:12:12,375 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-03 10:12:12,386 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:12:12,984 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:43,288 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-06-03 10:12:13,587 - rubix - DEBUG - SSP Wave: (5333,)\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:43,299 - rubix - INFO - Getting cosmology...\n", + "2025-06-03 10:12:13,608 - rubix - INFO - Getting cosmology...\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:43,348 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-06-03 10:12:14,224 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-06-03 10:06:43,393 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-03 10:06:43,393 - rubix - INFO - Compiling the expressions...\n", - "2025-06-03 10:06:43,394 - rubix - INFO - Number of devices: 32\n", - "2025-06-03 10:06:43,495 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-03 10:06:43,577 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-03 10:06:43,580 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-03 10:06:43,582 - rubix - INFO - Calculating IFU cube...\n", - "2025-06-03 10:06:43,582 - rubix - DEBUG - Input shapes: Metallicity: 4, Age: 4\n", - "2025-06-03 10:06:43,708 - rubix - DEBUG - Calculation Finished! Spectra shape: (4, 5994)\n", - "2025-06-03 10:06:43,709 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-06-03 10:06:43,714 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-06-03 10:06:43,714 - rubix - DEBUG - Doppler Shifted SSP Wave: (4, 5994)\n", - "2025-06-03 10:06:43,715 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-06-03 10:06:43,752 - rubix - INFO - Calculating Data Cube...\n", - "2025-06-03 10:06:43,755 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-06-03 10:06:43,755 - rubix - INFO - Convolving with PSF...\n", - "2025-06-03 10:06:43,761 - rubix - INFO - Convolving with LSF...\n", - "2025-06-03 10:06:43,765 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-03 10:06:52,006 - rubix - INFO - Pipeline run completed in 8.83 seconds.\n" + "2025-06-03 10:12:14,897 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-03 10:12:14,899 - rubix - INFO - Compiling the expressions...\n", + "2025-06-03 10:12:14,900 - rubix - INFO - Number of devices: 32\n", + "2025-06-03 10:12:15,117 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-03 10:12:15,255 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-03 10:12:15,262 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-03 10:12:15,280 - rubix - INFO - Calculating IFU cube...\n", + "2025-06-03 10:12:15,281 - rubix - DEBUG - Input shapes: Metallicity: 23118, Age: 23118\n", + "2025-06-03 10:12:15,537 - rubix - DEBUG - Calculation Finished! Spectra shape: (23118, 5333)\n", + "2025-06-03 10:12:15,538 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-06-03 10:12:15,545 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-06-03 10:12:15,546 - rubix - DEBUG - Doppler Shifted SSP Wave: (23118, 5333)\n", + "2025-06-03 10:12:15,546 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-06-03 10:12:15,600 - rubix - INFO - Calculating Data Cube...\n", + "2025-06-03 10:12:15,602 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-06-03 10:12:15,603 - rubix - INFO - Convolving with PSF...\n", + "2025-06-03 10:12:15,606 - rubix - INFO - Convolving with LSF...\n", + "2025-06-03 10:12:15,610 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-03 10:12:18,030 - rubix - INFO - Pipeline run completed in 6.25 seconds.\n" ] } ], @@ -550,7 +548,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -599,7 +597,7 @@ "outputs": [ { "data": { - "image/png": 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++KFuu+02FRQUqFOnTrUO2cnPz9fee+8d/vdBBx2kjIyMiBzml+feyspKbdq0Sd27d1ebNm1qPX+PHz8+4h6O7733njZs2KDf/e53Sk1NDc8fM2aMMjMzI9773HPPqWfPnurRo0f4PLpx40Yde+yxkqTXXntNksLn3ksvvTTi/ZMmTdrtNtqdqVOn1sijcnJyPMdtqF3lUfX5HmrzyxjVFVNHHnmktm3bps8++0yS9MEHH2j16tWaNGlSjWGLXvKo4cOHq1OnTuF/9+/fXwMGDAh/l5s3b9bChQt1xhlnhNu2ceNGbdq0SQUFBfriiy+0bt26qD8f8Ko++VNRUZHGjx+v0aNH691339XixYuVmpqq0047La6G6zPcDL5V/Qf57h6nXldnUteuXXf7Gd98840CgUCNZbt3717vdu6111415rVt21Y//vhj+N+u6+qee+7R/fffr9WrV0eM/a4u57alf//+nm9c7SUJqFZaWhpx/4PNmzfrhhtu0NNPP13j5pnVHTy789Zbb2natGkqLCzUtm3basTIzMwMH6TrU2bfEPvss0/Evx3HUffu3cP3Avjiiy8kSaNHj64zRnFxsdq2bWu1XUBDlZaWKhgMatmyZTVuxt+qVStJO/fvW2+9VbfccouKiorUvn17LViwQJJ2+ZAAAE3v0EMP1T/+8Q9VVFToww8/1AsvvKC7775bp512mpYvX679998/vGx9cpjt27dr+vTpeuSRR7Ru3bqIP3RqO3//Oqf65ptvJNU8j6akpNQ4nnzxxRdasWJFjftQVqvOH6rzt192cEnSfvvtV+v7GqJXr17Kz8/3FMNWHvXL3Lah30NtPvnkE/3hD3/QwoULa1xcrY7RWHmUJO2777569tlnJe28fYMxRtddd52uu+66WmNs2LAhoqMJaEz1yZ9mzpypzMzMiPvlPvHEE8rLy9PSpUvDF8j9jk4i+FZmZqY6duy420ep/+9//1OnTp2UkZERMb+xnqpV1xPPfnnyvuWWW3TdddfpvPPO00033RQe2z5p0qRGry5JS0vT9u3ba32tuuPl1xU4DfXtt9+quLg4orPtjDPO0JIlS3TVVVepT58+atWqlVzX1dChQ+u1DVatWqUhQ4aoR48euuuuu5SXl6fU1FS9/PLLuvvuuxu8HetK4EKhUFRPsav+/Ntvv119+vSpdZnqEwjQlA4++GCFQiFt2LChzvu9VQsGg+GE/KmnntLAgQPr/OMNgL+kpqbq0EMP1aGHHqp9991XY8eO1XPPPadp06aFl6lPDnPJJZfokUce0aRJkzRw4EBlZmbKcRydddZZtZ57veRfruuqV69euuuuu2p9/Zf3L2oq6enpMc+jKisr9fnnn0d01DT0e/i1LVu26Oijj1ZGRoZuvPFG7b333kpPT9f777+v3//+91HlUbVVRtR2E+z6qP78K6+8UgUFBbUu05CLuIBt9cmftm3bVqPiuvo4G08jCugkgq+ddNJJeuihh/Tmm2+Gn1D2S2+88Ya+/vprXXjhhVHF79y5s1zX1erVqyOucHz55ZdRt7k2zz//vI455hj97W9/i5i/ZcuWRr+pdOfOnbVy5cpaX6ue37lzZ0+f8fjjj0tS+CT/448/asGCBbrhhhs0derU8HLV1Te/VFfnzb/+9S+Vl5frxRdfjLjyWV16Xq36quLHH3+8y2Sibdu22rJlS43533zzTa2VEr9uqzFGX375pQ466KCIz83IyPB8BRLwqrS0NOI4tnr1ai1fvlxZWVnad999NXLkSI0aNUp33nmnDj74YP3www9asGCBDjroIJ144onauHGjnn/+eQ0ePFg7duzQI488oueee06LFy9uwrUCEK3qCuPvv/++we99/vnnNXr0aN15553heTt27Kj1HFqb6pziiy++CA8bk3Z2hKxevVq9e/cOz9t777314YcfasiQIbusxqnO31atWhVRPVRXfmNT586d9emnn9b6mq086vnnn9f27dsjOkvq+z3Utd0WLVqkTZs26R//+IeOOuqo8PzVq1dHLPfLPGpX+Uzbtm0jhiVWq64c+7Xacr7PP/88/CCE6twrJSWFPApNxmv+dOKJJ+ruu+/WjTfeqLPPPltbt27Vtddeq86dO+vggw9uwjVrGG4sAF+76qqr1Lx5c1144YURj0qXdg5f+t3vfqcWLVqEH5veUNUn3/vvvz9i/n333Rddg+sQDAZrXG157rnnmmRs9QknnKC3335by5Yti5i/ZcsWPfnkk+rTp4+nsfcLFy7UTTfdpK5du2rkyJGSfu5B//U2mDFjRo33t2zZMtyeX6otRnFxsR555JGI5Y4//ni1bt1a06dP144dOyJe++V79957b7399tuqqKgIz3vppZdqfay99PNTOao9//zz+v777zVs2DBJUt++fbX33nvrjjvuUGlpaY33//DDD7XGBWLhvffe08EHHxxOSCZPnqyDDz443En7yCOPaNSoUbriiiu03377afjw4Xr33XcjOmAfffRR9evXT4cffrg++eQTLVq0iCf0AT732muv1VrdUX3fl2iGY9WWw9x33331rhjp16+f2rdvr1mzZkWcc2fPnl3jXH/GGWdo3bp1euihh2rE2b59e/hpadXn3nvvvTdimdryCttOOOEEffvttxFPWpN2Phr7r3/9q7Kzs3XIIYdEHf/DDz/UpEmT1LZt24gnt9b3e2hIHlVRUVEjBz7kkEPUtWtXzZgxo0aMX+dRn332WUR+8+GHH+qtt96qdb3mzp0bkfe+8847Wrp0afi7zM7O1uDBg/WXv/yl1s5M8ig0Bq/507HHHqs5c+Zo7ty5OvjggzV06FClpaVp3rx5jTbKxQYqieBr++yzjx599FGNHDlSvXr10rhx49S1a1d9/fXX+tvf/qaNGzfqqaeeqjEmvb769u2rESNGaMaMGdq0aZMOO+wwLV68WJ9//rkkO2PKpZ0VUTfeeKPGjh2rQYMG6aOPPtKTTz7ZJPf2mDJlip577jkdddRRuvDCC9WjRw999913mj17tr7//vsanS678sorr+izzz5TVVWV1q9fr4ULF2r+/Pnq3LmzXnzxxXC5dUZGho466ijddtttqqysVKdOnfSf//ynxtUraed3Ikn/7//9P5111llKSUnRySefrOOPP16pqak6+eSTdeGFF6q0tFQPPfSQsrOzI5KJjIwM3X333Tr//PN16KGH6pxzzlHbtm314Ycfatu2bXr00Ucl7Xx07vPPP6+hQ4fqjDPO0KpVq/TEE0/UuS9lZWXpiCOO0NixY7V+/XrNmDFD3bt31/jx4yXtvJnvX//6Vw0bNkwHHHCAxo4dq06dOmndunV67bXXlJGRoX/961/13raAF4MHD97lDRJTUlJ0ww036IYbbqj19Xbt2qmwsDBWzQMQI5dccom2bdum3/72t+rRo4cqKiq0ZMkSPfPMM+rSpYvGjh3b4JgnnXSSHn/8cWVmZmr//fdXYWGh/vvf/9b7noopKSm6+eabdeGFF+rYY4/VmWeeqdWrV+uRRx6pkQede+65evbZZ/W73/1Or732mg4//HCFQiF99tlnevbZZ/Xqq6+qX79+6tOnj84++2zdf//9Ki4u1qBBg7RgwQLrleC1ueCCC/Twww/r9NNP13nnnaeDDz5YmzZt0jPPPKOPP/5Yjz32WMQNunfljTfe0I4dO8IPNXnrrbf04osvKjMzUy+88ELERbv6fg99+vRRMBjUrbfequLiYqWlpenYY4/VoEGD1LZtW40ePVqXXnqpHMfR448/XuNcEQgE9MADD+jkk09Wnz59NHbsWHXs2FGfffaZPvnkE7366quSpPPOO0933XWXCgoKNG7cOG3YsEGzZs3SAQccUON+R9LOoWJHHHGELrroIpWXl2vGjBnaY489dPXVV4eXmTlzpo444gj16tVL48ePV7du3bR+/XoVFhbq22+/1Ycffljv7wmIhtf8SZLOOuus8MM+4lbjPkwNiM7//vc/c/bZZ5uOHTualJQUk5OTY84++2zz0Ucf1Vi2+jH3P/zwQ52v/VJZWZmZMGGCycrKMq1atTLDhw83K1euNJLMn/70p/By1Y92rc9jUH/9WNAdO3aYK664wnTs2NE0b97cHH744aawsLDGctWPCY3m0an1eSx9tW+//dacf/75plOnTqZZs2YmKyvLnHTSSebtt9/e7Xt/+VnVU2pqqsnJyTHHHXecueeeeyIeFf/Lz/ztb39r2rRpYzIzM83pp59uvvvuu1ofVXvTTTeZTp06mUAgELHNX3zxRXPQQQeZ9PR006VLF3Prrbeahx9+uMb3Ur3soEGDTPPmzU1GRobp37+/eeqppyKWufPOO02nTp1MWlqaOfzww817771X4zup3tZPPfWUueaaa0x2drZp3ry5OfHEE80333xTYz0/+OADc+qpp5o99tjDpKWlmc6dO5szzjjDLFiwoF7bFgCAaL3yyivmvPPOMz169DCtWrUyqamppnv37uaSSy4x69evj1hWkpkwYUKNGJ07dzajR48O//vHH380Y8eONe3atTOtWrUyBQUF5rPPPqux3O7ykPvvv9907drVpKWlmX79+pnXX3+91seoV1RUmFtvvdUccMABJi0tzbRt29b07dvX3HDDDaa4uDi83Pbt282ll15q9thjD9OyZUtz8sknm7Vr19aaV/xadb51++23h+fV57H0v9wml19+uenatatJSUkxGRkZ5phjjjGvvPLKbt/7y8+qnlJSUkz79u3NUUcdZf74xz+aDRs21PqZ9fkejDHmoYceMt26dQs/gvu1114zxhjz1ltvmcMOO8w0b97c5Obmmquvvtq8+uqrEctUe/PNN81xxx1nWrdubVq2bGkOOuggc99990Us88QTT5hu3bqZ1NRU06dPH/Pqq6+a0aNHm86dO4eX+eW2vvPOO01eXp5JS0szRx55pPnwww9rrOeqVavMqFGjTE5OjklJSTGdOnUyJ510knn++ef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" ] From 199fd3f4037f42b345b616670477ee91425f699e Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 3 Jun 2025 10:42:54 +0200 Subject: [PATCH 34/76] merge conflicts --- rubix/spectra/dust/helpers.py | 1 - rubix/telescope/apertures.py | 4 +--- tests/test_telescope_factory.py | 4 +++- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/rubix/spectra/dust/helpers.py b/rubix/spectra/dust/helpers.py index 1c174b35..1139e32f 100644 --- a/rubix/spectra/dust/helpers.py +++ b/rubix/spectra/dust/helpers.py @@ -13,7 +13,6 @@ # Might come soon according to this github PR: https://github.com/jax-ml/jax/pull/18389 - def test_valid_x_range( wave: Float[Array, "n"], wave_range: Float[Array, "2"], outname: str ) -> None: # pragma no cover diff --git a/rubix/telescope/apertures.py b/rubix/telescope/apertures.py index 2b01cf99..53e33bb4 100644 --- a/rubix/telescope/apertures.py +++ b/rubix/telescope/apertures.py @@ -1,6 +1,4 @@ -""" This class defines the aperture mask for the observation of a galaxy. - -""" +""" This class defines the aperture mask for the observation of a galaxy.""" import jax.numpy as jnp import numpy as np diff --git a/tests/test_telescope_factory.py b/tests/test_telescope_factory.py index 81a9caf7..044159b4 100644 --- a/tests/test_telescope_factory.py +++ b/tests/test_telescope_factory.py @@ -12,7 +12,9 @@ SQUARE_APERTURE, ) from rubix.telescope.base import BaseTelescope -from rubix.telescope.factory import TelescopeFactory +from rubix.telescope.factory import ( + TelescopeFactory, +) jax.config.update("jax_platform_name", "cpu") From 8128662610a604bf9f42ca592aa99e258c10f061 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 3 Jun 2025 08:46:54 +0000 Subject: [PATCH 35/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- ...eline_single_function_shard_map_fits.ipynb | 149 ++------------ ...ine_single_function_shard_map_memory.ipynb | 193 ++---------------- rubix/core/ifu.py | 103 +++++----- rubix/core/pipeline.py | 2 +- rubix/spectra/ssp/fsps_grid.py | 2 +- rubix/telescope/apertures.py | 1 - 6 files changed, 90 insertions(+), 360 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb index d8c59572..028dd69c 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -13,17 +13,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0), CpuDevice(id=1)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -45,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -106,26 +98,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-29 12:44:22,344 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-05-29 12:44:22,344 - rubix - INFO - Rubix version: 0.0.post400+gee789d5.d20250306\n", - "2025-05-29 12:44:22,344 - rubix - INFO - JAX version: 0.5.0\n", - "2025-05-29 12:44:22,344 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -192,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -347,18 +322,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -366,64 +332,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-29 12:44:22,756 - rubix - INFO - Getting rubix data...\n", - "2025-05-29 12:44:22,756 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-05-29 12:44:22,804 - rubix - INFO - Centering stars particles\n", - "2025-05-29 12:44:23,331 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1000 for stars\n", - "2025-05-29 12:44:23,333 - rubix - INFO - Data loaded with 1000 star particles and 0 gas particles.\n", - "2025-05-29 12:44:23,333 - rubix - INFO - Setting up the pipeline...\n", - "2025-05-29 12:44:23,333 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-05-29 12:44:23,334 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-05-29 12:44:23,334 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-29 12:44:23,342 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-29 12:44:23,476 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-29 12:44:23,483 - rubix - INFO - Getting cosmology...\n", - "2025-05-29 12:44:23,502 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-29 12:44:23,541 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-29 12:44:23,549 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-05-29 12:44:23,713 - rubix - INFO - Assembling the pipeline...\n", - "2025-05-29 12:44:23,713 - rubix - INFO - Compiling the expressions...\n", - "2025-05-29 12:44:23,713 - rubix - INFO - Number of devices: 2\n", - "2025-05-29 12:44:23,840 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-05-29 12:44:23,880 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-05-29 12:44:23,882 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-05-29 12:44:23,889 - rubix - INFO - Calculating IFU cube...\n", - "2025-05-29 12:44:23,890 - rubix - DEBUG - Input shapes: Metallicity: 500, Age: 500\n", - "2025-05-29 12:44:23,981 - rubix - DEBUG - Calculation Finished! Spectra shape: (500, 5994)\n", - "2025-05-29 12:44:23,982 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-05-29 12:44:23,984 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-05-29 12:44:23,984 - rubix - DEBUG - Doppler Shifted SSP Wave: (500, 5994)\n", - "2025-05-29 12:44:23,984 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-05-29 12:44:24,010 - rubix - INFO - Calculating Data Cube...\n", - "2025-05-29 12:44:24,012 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-05-29 12:44:24,012 - rubix - INFO - Convolving with PSF...\n", - "2025-05-29 12:44:24,013 - rubix - INFO - Convolving with LSF...\n", - "2025-05-29 12:44:24,015 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-05-29 12:44:25,506 - rubix - INFO - Pipeline run completed in 2.17 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -433,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -449,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -472,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -500,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -521,20 +432,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -572,20 +472,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import numpy as np\n", diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index e6368d29..69be8573 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -16,17 +16,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -48,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -109,26 +101,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-06-03 10:12:03,586 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-06-03 10:12:03,587 - rubix - INFO - Rubix version: 0.0.post438+gd14bd2b.d20250603\n", - "2025-06-03 10:12:03,588 - rubix - INFO - JAX version: 0.6.0\n", - "2025-06-03 10:12:03,588 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -195,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -350,18 +325,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_NIHAO)" @@ -369,53 +335,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-06-03 10:12:04,695 - rubix - INFO - Getting rubix data...\n", - "2025-06-03 10:12:04,696 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-03 10:12:04,761 - rubix - INFO - Centering stars particles\n", - "2025-06-03 10:12:05,095 - rubix - INFO - Data loaded with 739749 star particles and 0 gas particles.\n", - "2025-06-03 10:12:05,096 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-03 10:12:05,096 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-03 10:12:05,097 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-03 10:12:05,099 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:05,569 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 10:12:05,711 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-03 10:12:05,720 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 10:12:06,180 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:06,666 - rubix - DEBUG - SSP Wave: (5333,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:06,678 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:07,161 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:07,782 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-03 10:12:07,783 - rubix - INFO - Compiling the expressions...\n", - "2025-06-03 10:12:07,784 - rubix - INFO - Number of devices: 32\n", - "2025-06-03 10:12:08,012 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-03 10:12:08,123 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-03 10:12:08,128 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-03 10:12:08,154 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-06-03 10:12:08,384 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-06-03 10:12:08,385 - rubix - INFO - Convolving with PSF...\n", - "2025-06-03 10:12:08,388 - rubix - INFO - Convolving with LSF...\n", - "2025-06-03 10:12:08,393 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-03 10:12:10,922 - rubix - INFO - Pipeline run completed in 5.83 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -425,62 +347,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:11,611 - rubix - INFO - Getting rubix data...\n", - "2025-06-03 10:12:11,613 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-03 10:12:11,717 - rubix - INFO - Centering stars particles\n", - "2025-06-03 10:12:11,775 - rubix - INFO - Data loaded with 739749 star particles and 0 gas particles.\n", - "2025-06-03 10:12:11,776 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-03 10:12:11,777 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-03 10:12:11,778 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-03 10:12:11,781 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:12,363 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 10:12:12,375 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-03 10:12:12,386 - rubix - INFO - Getting cosmology...\n", - "2025-06-03 10:12:12,984 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:13,587 - rubix - DEBUG - SSP Wave: (5333,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:13,608 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:14,224 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-03 10:12:14,897 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-03 10:12:14,899 - rubix - INFO - Compiling the expressions...\n", - "2025-06-03 10:12:14,900 - rubix - INFO - Number of devices: 32\n", - "2025-06-03 10:12:15,117 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-03 10:12:15,255 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-03 10:12:15,262 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-03 10:12:15,280 - rubix - INFO - Calculating IFU cube...\n", - "2025-06-03 10:12:15,281 - rubix - DEBUG - Input shapes: Metallicity: 23118, Age: 23118\n", - "2025-06-03 10:12:15,537 - rubix - DEBUG - Calculation Finished! Spectra shape: (23118, 5333)\n", - "2025-06-03 10:12:15,538 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-06-03 10:12:15,545 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-06-03 10:12:15,546 - rubix - DEBUG - Doppler Shifted SSP Wave: (23118, 5333)\n", - "2025-06-03 10:12:15,546 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-06-03 10:12:15,600 - rubix - INFO - Calculating Data Cube...\n", - "2025-06-03 10:12:15,602 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-06-03 10:12:15,603 - rubix - INFO - Convolving with PSF...\n", - "2025-06-03 10:12:15,606 - rubix - INFO - Convolving with LSF...\n", - "2025-06-03 10:12:15,610 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-03 10:12:18,030 - rubix - INFO - Pipeline run completed in 6.25 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "config_NIHAO[\"pipeline\"][\"name\"] = \"calc_ifu\"\n", @@ -492,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -501,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -522,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -543,20 +412,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -592,20 +450,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index ffcbfbca..51ab2d04 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -2,19 +2,19 @@ import jax import jax.numpy as jnp -from jax import lax from beartype import beartype as typechecker +from jax import lax from jaxtyping import Array, Float, jaxtyped from rubix import config as rubix_config from rubix.core.data import GasData, StarsData from rubix.logger import get_logger from rubix.spectra.ifu import ( + _velocity_doppler_shift_single, calculate_cube, cosmological_doppler_shift, resample_spectrum, velocity_doppler_shift, - _velocity_doppler_shift_single, ) from .data import RubixData @@ -369,45 +369,44 @@ def get_particle_spectrum(config: dict) -> Callable: # 2) prepare Doppler + resampling velocity_direction = rubix_config["ifu"]["doppler"]["velocity_direction"] - z_obs = config["galaxy"]["dist_z"] + z_obs = config["galaxy"]["dist_z"] # get telescope grid - telescope = get_telescope(config) - target_wavelength = telescope.wave_seq # shape (n_wave_tel,) + telescope = get_telescope(config) + target_wavelength = telescope.wave_seq # shape (n_wave_tel,) # get the SSP wavelengths for cosmological redshift ssp_model = get_ssp(config) ssp_wave0 = cosmological_doppler_shift( - z=z_obs, - wavelength=ssp_model.wavelength - ) # shape (n_wave_ssp,) + z=z_obs, wavelength=ssp_model.wavelength + ) # shape (n_wave_ssp,) @jaxtyped(typechecker=typechecker) def particle_spectrum( - age: Float[Array, ""], + age: Float[Array, ""], metallicity: Float[Array, ""], - mass: Float[Array, ""], - velocity: Float[Array, ""], + mass: Float[Array, ""], + velocity: Float[Array, ""], ) -> Float[Array, "n_wave_tel"]: # --- 1) SSP lookup - spec_ssp = lookup_ssp(metallicity, age) # (n_wave_ssp,) + spec_ssp = lookup_ssp(metallicity, age) # (n_wave_ssp,) # --- 2) mass scale - spec_mass = spec_ssp * mass # (n_wave_ssp,) + spec_mass = spec_ssp * mass # (n_wave_ssp,) # --- 3) Doppler‐shift the SSP wavelengths shifted_wave = velocity_doppler_shift( wavelength=ssp_wave0, velocity=velocity, direction=velocity_direction, - ) # (n_wave_ssp,) + ) # (n_wave_ssp,) # --- 4) resample onto telescope grid spec_tel = resample_spectrum( initial_spectrum=spec_mass, initial_wavelength=shifted_wave, target_wavelength=target_wavelength, - ) # (n_wave_tel,) + ) # (n_wave_tel,) return spec_tel @@ -417,14 +416,14 @@ def particle_spectrum( @jaxtyped(typechecker=typechecker) def get_calculate_datacube_laxscan(config: dict) -> Callable: """ - The function returns the function that calculates the datacube of the stars. + The function returns the function that calculates the datacube of the stars. It takes RubixData as input. It calculates the spectrum for one stellar particle, weights it by mass, doppler shifts it, resamples it to the telescope wavelength grid, and finally adds the spectrum at the right position in the datacube. This is done for every stellar particle in the RubixData object. This is done by using a JAX lax.scan, which is a more efficient way to do this than a for loop. - + Args: config (dict): The configuration dictionary @@ -443,20 +442,20 @@ def get_calculate_datacube_laxscan(config: dict) -> Callable: logger = get_logger(config.get("logger", None)) telescope = get_telescope(config) num_spaxels = int(telescope.sbin) - num_segments = num_spaxels ** 2 - wave_grid = telescope.wave_seq + num_segments = num_spaxels**2 + wave_grid = telescope.wave_seq # Bind the num_spaxels to the function # calculate_cube_fn = jax.tree_util.Partial(calculate_cube, num_spaxels=num_spaxels) # calculate_cube_pmap = jax.pmap(calculate_cube_fn) - + @jaxtyped(typechecker=typechecker) def calculate_datacube(rubixdata: RubixData) -> RubixData: logger.info("Calculating Data Cube...") # 1. extract arrays - specs = rubixdata.stars.spectra # (n_stars, n_wave) - pix = rubixdata.stars.pixel_assignment # (n_stars,) + specs = rubixdata.stars.spectra # (n_stars, n_wave) + pix = rubixdata.stars.pixel_assignment # (n_stars,) nstar = specs.shape[0] # initial empty cube: (num_segments, n_wave) @@ -464,16 +463,16 @@ def calculate_datacube(rubixdata: RubixData) -> RubixData: def scan_body(cube, i): # process the single spectrum - spec_i = specs[i] # shape (n_wave,) - pix_i = pix[i] # scalar in [0..nseg) + spec_i = specs[i] # shape (n_wave,) + pix_i = pix[i] # scalar in [0..nseg) # accumulate cube = cube.at[pix_i].add(spec_i) return cube, None - + # scan over all particle indices 0..n_particles-1 - cube_flat, _ = lax.scan(scan_body, - init_cube, - jnp.arange(nstar, dtype=jnp.int32)) + cube_flat, _ = lax.scan( + scan_body, init_cube, jnp.arange(nstar, dtype=jnp.int32) + ) # reshape to (n_spaxels, n_spaxels, n_wave) cube_3d = cube_flat.reshape(num_spaxels, num_spaxels, -1) @@ -484,6 +483,7 @@ def scan_body(cube, i): return calculate_datacube + @jaxtyped(typechecker=typechecker) def get_calculate_datacube_particlewise(config: dict) -> Callable: """ @@ -498,73 +498,68 @@ def get_calculate_datacube_particlewise(config: dict) -> Callable: telescope = get_telescope(config) ns = int(telescope.sbin) nseg = ns * ns - target_wave = telescope.wave_seq # (n_wave_tel,) + target_wave = telescope.wave_seq # (n_wave_tel,) # prepare SSP lookup lookup_ssp = get_lookup_interpolation(config) # prepare Doppler machinery velocity_direction = rubix_config["ifu"]["doppler"]["velocity_direction"] - z_obs = config["galaxy"]["dist_z"] + z_obs = config["galaxy"]["dist_z"] ssp_model = get_ssp(config) ssp_wave0 = cosmological_doppler_shift( - z=z_obs, - wavelength=ssp_model.wavelength - ) # (n_wave_ssp,) + z=z_obs, wavelength=ssp_model.wavelength + ) # (n_wave_ssp,) @jaxtyped(typechecker=typechecker) def calculate_datacube_particlewise(rubixdata: RubixData) -> RubixData: logger.info("Calculating Data Cube (combined per‐particle)…") stars = rubixdata.stars - ages = stars.age # (n_stars,) - metallicity = stars.metallicity # (n_stars,) - masses = stars.mass # (n_stars,) - velocities = stars.velocity # (n_stars,) - pix_idx = stars.pixel_assignment # (n_stars,) - nstar = ages.shape[0] + ages = stars.age # (n_stars,) + metallicity = stars.metallicity # (n_stars,) + masses = stars.mass # (n_stars,) + velocities = stars.velocity # (n_stars,) + pix_idx = stars.pixel_assignment # (n_stars,) + nstar = ages.shape[0] # init flat cube: (nseg, n_wave_tel) init_cube = jnp.zeros((nseg, target_wave.shape[-1])) def body(cube, i): - age_i = ages[i] # scalar - Z_i = metallicity[i] # scalar - m_i = masses[i] # scalar - v_i = velocities[i] # scalar or vector + age_i = ages[i] # scalar + Z_i = metallicity[i] # scalar + m_i = masses[i] # scalar + v_i = velocities[i] # scalar or vector pix_i = pix_idx[i].astype(jnp.int32) # 1) SSP lookup - spec_ssp = lookup_ssp(Z_i, age_i) # (n_wave_ssp,) + spec_ssp = lookup_ssp(Z_i, age_i) # (n_wave_ssp,) # 2) scale by mass - spec_mass = spec_ssp * m_i # (n_wave_ssp,) + spec_mass = spec_ssp * m_i # (n_wave_ssp,) # 3) Doppler‐shift wavelengths shifted_wave = _velocity_doppler_shift_single( wavelength=ssp_wave0, velocity=v_i, direction=velocity_direction, - ) # (n_wave_ssp,) + ) # (n_wave_ssp,) # 4) resample onto telescope grid spec_tel = resample_spectrum( initial_spectrum=spec_mass, initial_wavelength=shifted_wave, target_wavelength=target_wave, - ) # (n_wave_tel,) + ) # (n_wave_tel,) # 5) accumulate cube = cube.at[pix_i].add(spec_tel) return cube, None - cube_flat, _ = lax.scan( - body, - init_cube, - jnp.arange(nstar, dtype=jnp.int32) - ) + cube_flat, _ = lax.scan(body, init_cube, jnp.arange(nstar, dtype=jnp.int32)) cube_3d = cube_flat.reshape(ns, ns, -1) setattr(rubixdata.stars, "datacube", cube_3d) logger.debug(f"Datacube shape: {cube_3d.shape}") return rubixdata - #return jax.jit(calculate_datacube_particlewise) - return calculate_datacube_particlewise \ No newline at end of file + # return jax.jit(calculate_datacube_particlewise) + return calculate_datacube_particlewise diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 24dd27ad..2e52ffb5 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -32,10 +32,10 @@ from .dust import get_extinction from .ifu import ( get_calculate_datacube, + get_calculate_datacube_particlewise, get_calculate_spectra, get_doppler_shift_and_resampling, get_scale_spectrum_by_mass, - get_calculate_datacube_particlewise, ) from .lsf import get_convolve_lsf from .noise import get_apply_noise diff --git a/rubix/spectra/ssp/fsps_grid.py b/rubix/spectra/ssp/fsps_grid.py index b41ad1be..46230dba 100644 --- a/rubix/spectra/ssp/fsps_grid.py +++ b/rubix/spectra/ssp/fsps_grid.py @@ -112,7 +112,7 @@ def retrieve_ssp_data_from_fsps( _wave, _fluxes = sp.get_spectrum(zmet=zmet, tage=tage, peraa=peraa) spectrum_collector.append(_fluxes) ssp_wave = np.array(_wave) - offset = (_wave[1] - _wave[0]) / 2. + offset = (_wave[1] - _wave[0]) / 2.0 ssp_wave_centered = ssp_wave - offset ssp_flux = np.array(spectrum_collector) diff --git a/rubix/telescope/apertures.py b/rubix/telescope/apertures.py index 04b5cc69..e738cb16 100644 --- a/rubix/telescope/apertures.py +++ b/rubix/telescope/apertures.py @@ -1,6 +1,5 @@ """This class defines the aperture mask for the observation of a galaxy.""" - import jax.numpy as jnp import numpy as np from beartype import beartype as typechecker From af5d7d4de36b12b95b29433c0c26dc2d317c2672 Mon Sep 17 00:00:00 2001 From: anschaible Date: Thu, 5 Jun 2025 17:45:29 +0200 Subject: [PATCH 36/76] implement rotation matrix storage and read in for NIHAO --- .gitignore | 2 +- ...e_single_function_shard_map_fits_gsf.ipynb | 579 ++++++++++++++++++ rubix/core/rotation.py | 13 +- rubix/galaxy/alignment.py | 20 +- rubix/galaxy/input_handler/pynbody.py | 6 +- 5 files changed, 610 insertions(+), 10 deletions(-) create mode 100644 notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb diff --git a/.gitignore b/.gitignore index 3b11d146..5169cba6 100644 --- a/.gitignore +++ b/.gitignore @@ -174,7 +174,7 @@ rubix/spectra/ssp/templates/fsps.h5 notebooks/frames notebooks/frames/* notebooks/nohup.out - +notebooks/data # don´t add .env files *.env diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb new file mode 100644 index 00000000..3a00e1c8 --- /dev/null +++ b/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb @@ -0,0 +1,579 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from jax import config\n", + "#config.update(\"jax_enable_x64\", True)\n", + "config.update('jax_num_cpu_devices', 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0), CpuDevice(id=1)]\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import os\n", + "\n", + "# Tell XLA to fake 2 host CPU devices\n", + "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", + "\n", + "# Only make GPU 0 and GPU 1 visible to JAX:\n", + "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", + "\n", + "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", + "\n", + "import jax\n", + "\n", + "# Now JAX will list two CpuDevice entries\n", + "print(jax.devices())\n", + "# → [CpuDevice(id=0), CpuDevice(id=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "#import os\n", + "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", + "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-06-05 15:11:51,991 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-06-05 15:11:51,992 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", + "2025-06-05 15:11:51,993 - rubix - INFO - JAX version: 0.6.0\n", + "2025-06-05 15:11:51,993 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "\n", + "galaxy_id = \"g7.66e11\"\n", + "\n", + "config_NIHAO = {\n", + " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"NihaoHandler\",\n", + " \"args\": {\n", + " \"particle_type\": [\"stars\"],\n", + " \"save_data_path\": \"data\",\n", + " \"snapshot\": \"1024\",\n", + " },\n", + " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", + " \"subset\": {\"use_subset\": False, \"subset_size\": 200000},\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"NIHAO\",\n", + " \"args\": {\n", + " \"path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024',\n", + " \"halo_path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024.z0.000.AHF_halos',\n", + " \"halo_id\": 0,\n", + " },\n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE_WFM\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.01,\n", + " \"rotation\": {\"type\": \"matrix\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"BruzualCharlot2003\" #\"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Pipeline yaml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the pipeline\n", + "\n", + "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config_NIHAO)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-06-05 15:11:52,397 - rubix - INFO - Getting rubix data...\n", + "2025-06-05 15:11:52,408 - rubix - INFO - Loading data into input handler\n", + "2025-06-05 15:11:52,410 - rubix - INFO - Using PynbodyHandler to load a NIHAO galaxy\n", + "2025-06-05 15:11:52,418 - rubix - INFO - Galaxy redshift (dist_z) set to: 0.01\n", + "2025-06-05 15:11:52,445 - rubix - INFO - Simulation snapshot loaded from halo 0\n", + "2025-06-05 15:11:52,554 - rubix - INFO - Halo data loaded.\n", + "2025-06-05 15:11:57,921 - rubix - INFO - Applying face-on rotation to halo 0 with rotation matrix: faceon\n", + "2025-06-05 15:11:57,922 - rubix - INFO - Edge-on rotation matrix: sideon\n", + "2025-06-05 15:11:57,995 - rubix - WARNING - Field 'sfr' -> 'sfr' not found for gas. Assigning zeros.\n", + "2025-06-05 15:11:57,997 - rubix - WARNING - Field 'internal_energy' -> 'u' not found for gas. Assigning zeros.\n", + "2025-06-05 15:11:57,997 - rubix - WARNING - Field 'electron_abundance' -> 'electron_abundance' not found for gas. Assigning zeros.\n", + "2025-06-05 15:11:58,052 - rubix - INFO - Metals assigned to gas particles.\n", + "2025-06-05 15:11:58,052 - rubix - INFO - Metals shape is: (138872, 10)\n", + "2025-06-05 15:11:58,053 - rubix - INFO - Simulation snapshot and halo data loaded successfully for classes: ['stars', 'gas'].\n", + "2025-06-05 15:11:58,054 - rubix - DEBUG - Converting to Rubix format..\n", + "2025-06-05 15:11:58,130 - rubix - INFO - Half-mass radius calculated: 1.81 kpc\n", + "2025-06-05 15:11:58,131 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-06-05 15:11:58,132 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", + "2025-06-05 15:11:58,134 - rubix - DEBUG - Converting redshift for galaxy data into \n", + "2025-06-05 15:11:58,136 - rubix - DEBUG - Converting center for galaxy data into kpc\n", + "2025-06-05 15:11:58,136 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", + "2025-06-05 15:11:58,137 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", + "2025-06-05 15:11:58,146 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", + "2025-06-05 15:11:58,150 - rubix - DEBUG - Converting metallicity for particle type stars into \n", + "2025-06-05 15:11:58,155 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", + "2025-06-05 15:11:58,167 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", + "2025-06-05 15:11:58,175 - rubix - DEBUG - Converting density for particle type gas into Msun/kpc^3\n", + "2025-06-05 15:11:58,177 - rubix - DEBUG - Converting temperature for particle type gas into K\n", + "2025-06-05 15:11:58,179 - rubix - DEBUG - Converting metals for particle type gas into \n", + "2025-06-05 15:11:58,189 - rubix - DEBUG - Converting metallicity for particle type gas into \n", + "2025-06-05 15:11:58,191 - rubix - DEBUG - Converting coords for particle type gas into kpc\n", + "2025-06-05 15:11:58,198 - rubix - DEBUG - Converting velocity for particle type gas into km/s\n", + "2025-06-05 15:11:58,201 - rubix - DEBUG - Converting mass for particle type gas into Msun\n", + "2025-06-05 15:11:58,202 - rubix - DEBUG - Converting sfr for particle type gas into Msun/yr\n", + "2025-06-05 15:11:58,204 - rubix - DEBUG - Converting internal_energy for particle type gas into erg/g\n", + "2025-06-05 15:11:58,205 - rubix - DEBUG - Converting electron_abundance for particle type gas into \n", + "2025-06-05 15:11:58,206 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-06-05 15:11:58,272 - rubix - INFO - Centering stars particles\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted to Rubix format!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-06-05 15:11:58,790 - rubix - INFO - Data loaded with 911988 star particles and 0 gas particles.\n", + "2025-06-05 15:11:58,792 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-05 15:11:58,792 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-05 15:11:58,794 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-05 15:11:58,797 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-05 15:11:59,584 - rubix - INFO - Getting cosmology...\n", + "2025-06-05 15:11:59,822 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-05 15:11:59,837 - rubix - INFO - Getting cosmology...\n", + "2025-06-05 15:11:59,854 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-05 15:11:59,935 - rubix - DEBUG - SSP Wave: (842,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-05 15:11:59,957 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-05 15:12:00,007 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-05 15:12:00,201 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-05 15:12:00,202 - rubix - INFO - Compiling the expressions...\n", + "2025-06-05 15:12:00,202 - rubix - INFO - Number of devices: 2\n", + "2025-06-05 15:12:00,377 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-05 15:12:00,539 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-05 15:12:00,546 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-05 15:12:00,584 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-06-05 15:12:01,048 - rubix - DEBUG - Datacube shape: (300, 300, 3721)\n", + "2025-06-05 15:12:01,049 - rubix - INFO - Convolving with PSF...\n", + "2025-06-05 15:12:01,053 - rubix - INFO - Convolving with LSF...\n", + "2025-06-05 15:12:01,060 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-05 15:12:03,493 - rubix - INFO - Pipeline run completed in 4.70 seconds.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "inputdata = pipe.prepare_data()\n", + "rubixdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[2.1107967e+00 2.1986437e+00 2.1976156e+00 ... 2.6971066e-01\n", + " 2.7005145e-01 2.5941366e-01]\n", + " [4.3303677e+01 4.5119213e+01 4.5112061e+01 ... 9.7780552e+00\n", + " 9.7862215e+00 9.3985033e+00]\n", + " [1.7048402e+02 1.7763306e+02 1.7760628e+02 ... 3.8974445e+01\n", + " 3.9006832e+01 3.7461323e+01]\n", + " ...\n", + " [1.2704880e+02 1.3227231e+02 1.3214366e+02 ... 1.0185113e+01\n", + " 1.0195188e+01 9.7927179e+00]\n", + " [3.1679897e+01 3.2982395e+01 3.2950314e+01 ... 2.5396807e+00\n", + " 2.5421925e+00 2.4418359e+00]\n", + " [4.9116063e-01 5.1135427e-01 5.1085693e-01 ... 3.9374843e-02\n", + " 3.9413784e-02 3.7857872e-02]]\n", + "\n", + " [[9.4020721e+01 9.7920258e+01 9.7860497e+01 ... 7.7395754e+00\n", + " 7.7535276e+00 7.4503202e+00]\n", + " [7.5969894e+01 7.9127907e+01 7.9087097e+01 ... 7.8218651e+00\n", + " 7.8308487e+00 7.5221119e+00]\n", + " [5.3958618e+01 5.6216671e+01 5.6203354e+01 ... 1.0667229e+01\n", + " 1.0676281e+01 1.0253429e+01]\n", + " ...\n", + " [3.2539127e+01 3.3877522e+01 3.3845169e+01 ... 2.6096947e+00\n", + " 2.6122832e+00 2.5091665e+00]\n", + " [8.1137037e+00 8.4474344e+00 8.4393673e+00 ... 6.5073311e-01\n", + " 6.5137857e-01 6.2566626e-01]\n", + " [1.2579371e-01 1.3096783e-01 1.3084275e-01 ... 1.0088872e-02\n", + " 1.0098881e-02 9.7002396e-03]]\n", + "\n", + " [[3.7715341e+02 3.9279553e+02 3.9255539e+02 ... 3.0950544e+01\n", + " 3.1006439e+01 2.9793962e+01]\n", + " [2.6321646e+02 2.7414255e+02 2.7398477e+02 ... 2.1766554e+01\n", + " 2.1794605e+01 2.0937229e+01]\n", + " [4.6997608e+01 4.8949516e+01 4.8922432e+01 ... 3.9907115e+00\n", + " 3.9947922e+00 3.8371468e+00]\n", + " ...\n", + " [5.6084053e+01 5.8426796e+01 5.8408527e+01 ... 4.5696249e+00\n", + " 4.5746212e+00 4.3944702e+00]\n", + " [1.3944356e+01 1.4526852e+01 1.4522321e+01 ... 1.1360933e+00\n", + " 1.1373357e+00 1.0925472e+00]\n", + " [2.1614985e-01 2.2517905e-01 2.2510880e-01 ... 1.7610380e-02\n", + " 1.7629640e-02 1.6935380e-02]]\n", + "\n", + " ...\n", + "\n", + " [[1.2135274e+01 1.2638837e+01 1.2631381e+01 ... 6.4981157e-01\n", + " 6.5124941e-01 6.2627065e-01]\n", + " [5.5483776e+01 5.7784695e+01 5.7749126e+01 ... 3.1669793e+00\n", + " 3.1733642e+00 3.0510781e+00]\n", + " [4.8090885e+01 5.0077457e+01 5.0038528e+01 ... 3.5894508e+00\n", + " 3.5939960e+00 3.4530318e+00]\n", + " ...\n", + " [2.0837215e+02 2.1699840e+02 2.1684923e+02 ... 1.7300163e+01\n", + " 1.7316896e+01 1.6632938e+01]\n", + " [5.4272240e+01 5.6520248e+01 5.6482670e+01 ... 4.5333900e+00\n", + " 4.5377030e+00 4.3584142e+00]\n", + " [4.2790710e+01 4.4562160e+01 4.4531502e+01 ... 3.6314108e+00\n", + " 3.6348450e+00 3.4912102e+00]]\n", + "\n", + " [[4.6256355e+01 4.8176720e+01 4.8149277e+01 ... 2.4131727e+00\n", + " 2.4187646e+00 2.3262236e+00]\n", + " [1.8563860e+02 1.9334552e+02 1.9323532e+02 ... 9.6885986e+00\n", + " 9.7110338e+00 9.3394794e+00]\n", + " [4.6820892e+01 4.8764538e+01 4.8736599e+01 ... 2.4593067e+00\n", + " 2.4649472e+00 2.3705857e+00]\n", + " ...\n", + " [7.3373207e+01 7.6411057e+01 7.6358841e+01 ... 6.0944810e+00\n", + " 6.1003828e+00 5.8594475e+00]\n", + " [9.6186623e+01 1.0017204e+02 1.0010677e+02 ... 7.9991369e+00\n", + " 8.0068121e+00 7.6905146e+00]\n", + " [1.7704167e+02 1.8437042e+02 1.8424315e+02 ... 1.4692220e+01\n", + " 1.4706187e+01 1.4125123e+01]]\n", + "\n", + " [[1.1533578e+01 1.2012404e+01 1.2005561e+01 ... 6.0168535e-01\n", + " 6.0307962e-01 5.8000606e-01]\n", + " [4.6254158e+01 4.8174438e+01 4.8146996e+01 ... 2.4129939e+00\n", + " 2.4185853e+00 2.3260510e+00]\n", + " [1.1533578e+01 1.2012404e+01 1.2005561e+01 ... 6.0168535e-01\n", + " 6.0307962e-01 5.8000606e-01]\n", + " ...\n", + " [8.4760414e+01 8.8270599e+01 8.8211227e+01 ... 7.0483670e+00\n", + " 7.0552197e+00 6.7765970e+00]\n", + " [1.8717400e+02 1.9493661e+02 1.9481715e+02 ... 1.5595144e+01\n", + " 1.5610263e+01 1.4993746e+01]\n", + " [8.5281387e+01 8.8815063e+01 8.8757294e+01 ... 7.0918350e+00\n", + " 7.0986452e+00 6.8182302e+00]]]\n" + ] + } + ], + "source": [ + "#print(rubixdata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert luminosity to flux" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from rubix.spectra.ifu import convert_luminoisty_to_flux\n", + "from rubix.cosmology import PLANCK15\n", + "\n", + "observation_lum_dist = PLANCK15.luminosity_distance_to_z(config_NIHAO[\"galaxy\"][\"dist_z\"])\n", + "observation_z = config_NIHAO[\"galaxy\"][\"dist_z\"]\n", + "pixel_size = 1.0\n", + "fluxcube = convert_luminoisty_to_flux(rubixdata, observation_lum_dist, observation_z, pixel_size)\n", + "rubixdata = fluxcube/1e-20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Store datacube in a fits file with header" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "#from rubix.core.fits import store_fits\n", + "\n", + "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_ultraWFM\":\n", + "# cutted_datatcube = data.stars.datacube[300:600, :, :]\n", + "# data.stars.datacube = cutted_datatcube\n", + "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_WFM\":\n", + "# cutted_datatcube = data.stars.datacube[100:200, :, :]\n", + "# data.stars.datacube = cutted_datatcube\n", + "\n", + "#store_fits(config_NIHAO, rubixdata, \"./output/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Mock-data\n", + "\n", + "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import jax.numpy as jnp\n", + "\n", + "wave = pipe.telescope.wave_seq\n", + "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", + "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "wave = pipe.telescope.wave_seq\n", + "\n", + "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", + "spectra_sharded = rubixdata # Spectra of all stars\n", + "#print(spectra.shape)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "#plt.subplot(1, 2, 1)\n", + "#plt.title(\"Rubix\")\n", + "#plt.xlabel(\"Wavelength [Angstrom]\")\n", + "#plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "#plt.plot(wave, spectra[12,12,:])\n", + "#plt.plot(wave, spectra[8,12,:])\n", + "\n", + "#plt.subplot(1, 2, 2)\n", + "plt.title(\"Rubix Sharded\")\n", + "plt.xlabel(\"Wavelength [Angstrom]\")\n", + "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", + "plt.plot(wave, spectra_sharded[21,15,:])\n", + "plt.plot(wave, spectra_sharded[15,21,:])\n", + "plt.plot(wave, spectra_sharded[13,4,:])\n", + "plt.plot(wave, spectra_sharded[4,13,:])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot a spacial image of the data cube" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#NBVAL_SKIP\n", + "import numpy as np\n", + "# get the spectra of the visible wavelengths from the ifu cube\n", + "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", + "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", + "#visible_spectra.shape\n", + "\n", + "#image = jnp.sum(visible_spectra, axis=2)\n", + "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", + "img32 = np.array(sharded_image, dtype=np.float32)\n", + "\n", + "# Plot side by side\n", + "plt.figure(figsize=(6, 5))\n", + "\n", + "# Original IFU datacube image\n", + "#im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", + "#axes[0].set_title(\"Original IFU Datacube\")\n", + "#fig.colorbar(im0, ax=axes[0])\n", + "\n", + "# Sharded IFU datacube image\n", + "plt.imshow(img32, origin=\"lower\", cmap=\"inferno\", vmin=0, vmax=1e5)\n", + "plt.title(\"Sharded IFU Datacube\")\n", + "plt.colorbar(label=\"Flux [erg/s/cm^2]\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DONE!\n", + "\n", + "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/rubix/core/rotation.py b/rubix/core/rotation.py index 035931c3..41ba090e 100644 --- a/rubix/core/rotation.py +++ b/rubix/core/rotation.py @@ -1,5 +1,6 @@ from beartype import beartype as typechecker from jaxtyping import jaxtyped +import jax.numpy as jnp from rubix.galaxy.alignment import rotate_galaxy as rotate_galaxy_core from rubix.logger import get_logger @@ -42,7 +43,7 @@ def get_galaxy_rotation(config: dict): # Check if type is provided if "type" in config["galaxy"]["rotation"]: # Check if type is valid: face-on or edge-on - if config["galaxy"]["rotation"]["type"] not in ["face-on", "edge-on"]: + if config["galaxy"]["rotation"]["type"] not in ["face-on", "edge-on", "matrix"]: raise ValueError("Invalid type provided in rotation information") # if type is face on, alpha = beta = gamma = 0 @@ -95,6 +96,15 @@ def rotate_galaxy(rubixdata: RubixData) -> RubixData: ), f"Velocities not found for {particle_type}. " assert masses is not None, f"Masses not found for {particle_type}. " + if config["galaxy"]["rotation"]=="matrix": + + rot_np = jnp.load('./data/rotation_matrix.npy') + rot_jax = jnp.array(rot_np) + logger.info(f"Using rotation matrix from file: {rot_jax}.") + rotation_matrix = rot_jax + else: + rotation_matrix = None + # Rotate the galaxy coords, velocities = rotate_galaxy_core( positions=coords, @@ -104,6 +114,7 @@ def rotate_galaxy(rubixdata: RubixData) -> RubixData: alpha=alpha, beta=beta, gamma=gamma, + R=rotation_matrix, ) # Update the inputs diff --git a/rubix/galaxy/alignment.py b/rubix/galaxy/alignment.py index 1398384a..54854f8d 100644 --- a/rubix/galaxy/alignment.py +++ b/rubix/galaxy/alignment.py @@ -238,6 +238,7 @@ def rotate_galaxy( alpha: float, beta: float, gamma: float, + R=None, # type: Float[Array, "3 3"] = None ) -> Tuple[Float[Array, "* 3"], Float[Array, "* 3"]]: """ Orientate the galaxy by applying a rotation matrix to the positions of the particles. @@ -254,12 +255,17 @@ def rotate_galaxy( Returns: The rotated positions and velocities as a jnp.ndarray. """ - - I = moment_of_inertia_tensor(positions, masses, halfmass_radius) - R = rotation_matrix_from_inertia_tensor(I) - pos_rot = apply_init_rotation(positions, R) - vel_rot = apply_init_rotation(velocities, R) - pos_final = apply_rotation(pos_rot, alpha, beta, gamma) - vel_final = apply_rotation(vel_rot, alpha, beta, gamma) + if R is None: + I = moment_of_inertia_tensor(positions, masses, halfmass_radius) + R = rotation_matrix_from_inertia_tensor(I) + pos_rot = apply_init_rotation(positions, R) + vel_rot = apply_init_rotation(velocities, R) + pos_final = apply_rotation(pos_rot, alpha, beta, gamma) + vel_final = apply_rotation(vel_rot, alpha, beta, gamma) + else: + pos_rot = apply_init_rotation(positions, R) + vel_rot = apply_init_rotation(velocities, R) + pos_final = apply_rotation(pos_rot, alpha, beta, gamma) + vel_final = apply_rotation(vel_rot, alpha, beta, gamma) return pos_final, vel_final diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index d2078118..89904c4c 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -73,7 +73,11 @@ def load_data(self): self.logger.info(f"Simulation snapshot loaded from halo {self.halo_id}") halo = self.get_halo_data(halo_id=self.halo_id) if halo is not None: - pynbody.analysis.angmom.faceon(halo) + pynbody.analysis.angmom.faceon(halo.s) + ang_mom_vec = pynbody.analysis.angmom.ang_mom_vec(halo.s) + rotation_matrix = pynbody.analysis.angmom.calc_sideon_matrix(ang_mom_vec) + np.save('./data/rotation_matrix.npy', rotation_matrix) + self.logger.info("Rotation matrix calculated and saved to '/notebooks/data/rotation_matrix.npy'.") self.sim = halo fields = self.pynbody_config["fields"] From 3871f2e48f7cec371c1c3bcf42a94cb5b37a437d Mon Sep 17 00:00:00 2001 From: anschaible Date: Fri, 6 Jun 2025 12:24:57 +0200 Subject: [PATCH 37/76] pytests --- .gitignore | 1 + ...ine_single_function_shard_map_memory.ipynb | 199 ++++++++++++++++-- rubix/core/ifu.py | 188 ++--------------- rubix/core/pipeline.py | 3 +- rubix/spectra/ssp/fsps_grid.py | 14 +- tests/test_core_ifu.py | 56 +---- tests/test_core_pipeline.py | 26 ++- 7 files changed, 236 insertions(+), 251 deletions(-) diff --git a/.gitignore b/.gitignore index 3b11d146..bc294be1 100644 --- a/.gitignore +++ b/.gitignore @@ -174,6 +174,7 @@ rubix/spectra/ssp/templates/fsps.h5 notebooks/frames notebooks/frames/* notebooks/nohup.out +notebooks/data/gsf # don´t add .env files diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index 69be8573..1aa2eba5 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -16,9 +16,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)]\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -40,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -101,9 +109,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-06-06 11:39:49,068 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-06-06 11:39:49,069 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", + "2025-06-06 11:39:49,069 - rubix - INFO - JAX version: 0.6.0\n", + "2025-06-06 11:39:49,070 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -128,7 +153,7 @@ " \"snapshot\": \"1024\",\n", " },\n", " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": False, \"subset_size\": 100},\n", + " \"subset\": {\"use_subset\": True, \"subset_size\": 100},\n", " },\n", " \"simulation\": {\n", " \"name\": \"NIHAO\",\n", @@ -155,7 +180,7 @@ " \n", " \"ssp\": {\n", " \"template\": {\n", - " \"name\": \"Mastar_CB19_SLOG_1_5\"\n", + " \"name\": \"FSPS\" #\"Mastar_CB19_SLOG_1_5\"\n", " },\n", " \"dust\": {\n", " \"extinction_model\": \"Cardelli89\",\n", @@ -170,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -325,9 +350,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_NIHAO)" @@ -335,9 +369,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-06-06 11:39:49,528 - rubix - INFO - Getting rubix data...\n", + "2025-06-06 11:39:49,530 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-06 11:39:49,601 - rubix - INFO - Centering stars particles\n", + "2025-06-06 11:39:50,769 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100 for stars\n", + "2025-06-06 11:39:50,770 - rubix - INFO - Data loaded with 100 star particles and 0 gas particles.\n", + "2025-06-06 11:39:50,771 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-06 11:39:50,772 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-06 11:39:50,774 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-06 11:39:50,776 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:39:50,798 - rubix - INFO - Getting cosmology...\n", + "2025-06-06 11:39:50,964 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-06 11:39:50,973 - rubix - INFO - Getting cosmology...\n", + "2025-06-06 11:39:50,994 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:39:51,053 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:39:51,065 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:39:51,118 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:39:51,311 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-06 11:39:51,312 - rubix - INFO - Compiling the expressions...\n", + "2025-06-06 11:39:51,313 - rubix - INFO - Number of devices: 32\n", + "2025-06-06 11:39:51,505 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-06 11:39:51,613 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-06 11:39:51,618 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-06 11:39:51,645 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-06-06 11:39:51,877 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-06-06 11:39:51,878 - rubix - INFO - Convolving with PSF...\n", + "2025-06-06 11:39:51,881 - rubix - INFO - Convolving with LSF...\n", + "2025-06-06 11:39:51,887 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-06 11:40:00,098 - rubix - INFO - Pipeline run completed in 9.33 seconds.\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "\n", @@ -347,9 +426,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:40:00,263 - rubix - INFO - Getting rubix data...\n", + "2025-06-06 11:40:00,264 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-06-06 11:40:00,292 - rubix - INFO - Centering stars particles\n", + "2025-06-06 11:40:01,096 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100 for stars\n", + "2025-06-06 11:40:01,109 - rubix - INFO - Data loaded with 100 star particles and 0 gas particles.\n", + "2025-06-06 11:40:01,110 - rubix - INFO - Setting up the pipeline...\n", + "2025-06-06 11:40:01,111 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-06-06 11:40:01,111 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-06-06 11:40:01,113 - rubix - INFO - Calculating spatial bin edges...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:40:01,124 - rubix - INFO - Getting cosmology...\n", + "2025-06-06 11:40:01,133 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-06-06 11:40:01,143 - rubix - INFO - Getting cosmology...\n", + "2025-06-06 11:40:01,170 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:40:01,208 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:40:01,221 - rubix - INFO - Getting cosmology...\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:40:01,274 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-06-06 11:40:01,321 - rubix - INFO - Assembling the pipeline...\n", + "2025-06-06 11:40:01,322 - rubix - INFO - Compiling the expressions...\n", + "2025-06-06 11:40:01,323 - rubix - INFO - Number of devices: 32\n", + "2025-06-06 11:40:01,430 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-06-06 11:40:01,511 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-06-06 11:40:01,514 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-06-06 11:40:01,516 - rubix - INFO - Calculating IFU cube...\n", + "2025-06-06 11:40:01,516 - rubix - DEBUG - Input shapes: Metallicity: 4, Age: 4\n", + "2025-06-06 11:40:01,640 - rubix - DEBUG - Calculation Finished! Spectra shape: (4, 5994)\n", + "2025-06-06 11:40:01,641 - rubix - INFO - Scaling Spectra by Mass...\n", + "2025-06-06 11:40:01,646 - rubix - INFO - Doppler shifting and resampling spectra...\n", + "2025-06-06 11:40:01,646 - rubix - DEBUG - Doppler Shifted SSP Wave: (4, 5994)\n", + "2025-06-06 11:40:01,647 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", + "2025-06-06 11:40:01,680 - rubix - INFO - Calculating Data Cube...\n", + "2025-06-06 11:40:01,682 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", + "2025-06-06 11:40:01,683 - rubix - INFO - Convolving with PSF...\n", + "2025-06-06 11:40:01,686 - rubix - INFO - Convolving with LSF...\n", + "2025-06-06 11:40:01,689 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-06-06 11:40:10,033 - rubix - INFO - Pipeline run completed in 8.92 seconds.\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "config_NIHAO[\"pipeline\"][\"name\"] = \"calc_ifu\"\n", @@ -361,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -370,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -391,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -412,9 +545,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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+/bVq1SqtW7fusM8PeOHn8qSD/e1vf9MxxxxzyNEvsYZhZYh55X+I/9yy6IfqRDp4xa7KfPPNN0pISKhwbNu2bavczpYtW1bY16hRI/3444+hx8FgUA8++KAeeeQRFRQUhI35Li/ntqVHjx41npC6JklBuR07doTNe/DDDz/o9ttv1wsvvFBhsszyjp2f895772nChAlatGiRdu7cWSFGWlqa1qxZI8n+Cm1HH3102GOfz6e2bduG5gAo/3IYNmzYIWMUFRWpUaNGVtsF1NS3336radOm6dtvvw0Ncb3ppps0a9YsTZs2TXfffbdyc3P19ttv6+KLL9ZVV12lsrIy9erVK5TsA6h93bt317/+9S/t2bNHy5Yt08yZM/XAAw/ooosu0tKlS3X88ceHjq1KrrJr1y7l5+dr2rRpWrduXdiQ/Mq+pw/Onb755htJFb8v69atW2HRkFWrVunLL7+sMJ9kufI8oTxPO7BjS5KOPfbYSn+uOjp06KB+/frVKIatfOnAHLa670NlPv/8c/3pT3/S3LlzK9w8LY9RW/mSJB1zzDF66aWXJO2fjsEYo9tuu0233XZbpTE2bdoU1sEERFNV8qQD7d69W//4xz8Oe4XsaKBzCDEvLS1NzZo106effhrxuE8//VTNmzdXIBAI219bq2QdagWzA7/M7777bt1222268sordeedd4bGtI8ZM6bWq0n8fr927dpV6XPlHS4HV9xU13fffaeioqKwTraLL75Y77//vn7/+9+rc+fOatCggYLBoAYMGFCla7BmzRqdccYZateune6//35lZ2crKSlJ//73v/XAAw9U+zoeKqErKys7rFXpys8/adIkde7cudJjGjRoUO24gNc+++wzlZWV6ZhjjgnbX1paGuq83rBhg0aNGqVhw4Zp6NCh2r59u8aPH6+LLrpIs2fPtvIHEoDDk5SUpO7du6t79+465phjNGLECM2YMUMTJkwIHVOVXOXaa6/VtGnTNGbMGPXq1UtpaWny+Xy69NJLK/2OrUmeFQwG1aFDB91///2VPn/g/ETRkpyc7Hm+tHfvXn311VdhHTTVfR8Otm3bNp122mkKBAK644471KZNGyUnJ+vjjz/WH/7wh8PKl0wlc3dWNrl1VZSf/6abblL//v0rPaY6N2kBr1UlTzrQzJkztX379og3jGMNnUOIC4MGDdITTzyhd999N7Ti2IHeeecdrV27VlddddVhxc/JyVEwGFRBQUHYnY7Vq1cfdpsr8/LLL6tv37568sknw/Zv27at1ieLzsnJ0cqVKyt9rnx/Tk5Ojc7x97//XZJCX/o//vij5syZo9tvv13jx48PHVdZKeah/sh8/fXXVVpaqtdeey3sDmh56Xm58ruLy5cvj5hcNGrUSNu2bauw/5tvvqlwh7OythpjtHr1anXs2DHsvIFAoMZ3IoHatGPHDiUmJmrJkiUV/oAs79CcMmWK0tLSwlaGfPbZZ5Wdna0PPvggNEQUQHSVVw5///331f7Zl19+WcOGDdNf//rX0L7du3dX+l1ZmfLcYdWqVaHhYdL+DpCCggJ16tQptK9NmzZatmyZzjjjjIidy+V52po1a8KqhQ6Vx9iUk5OjL774otLnbOVLL7/8snbt2hXWSVLV9+FQ123+/PnaunWr/vWvf+nUU08N7S8oKAg77sB8KVLe0qhRo7Dhh+XKK8UOVllu99VXX4VWNivPserWrUu+hLhQlTzpQH/72980aNCg0ATs8YA5hxAXfv/73yslJUVXXXVV2JLn0v5hSldffbXq1asXWv68usq/jB955JGw/Q8//PDhNfgQEhMTK9x1mTFjRlTGVP/yl7/U4sWLtWTJkrD927Zt0z/+8Q917ty5RmPu586dqzvvvFO5ubm6/PLLJf3vjuXB12Dy5MkVfr58yceDk6DKYhQVFWnatGlhx5111llKTU1Vfn6+du/eHfbcgT/bpk0bLV68WHv27Ante+ONNypdnl763+ob5V5++WV9//33GjhwoKT9c7K0adNG9913n3bs2FHh5zdv3lxpXCDaunTporKyMm3atElt27YN28p/F+zcubPCCj7l/yaZSwuoffPmzau0mqN8qOfhDLuqLFd5+OGHq1wh0q1bN2VkZOjRRx8N+26dPn16he/0iy++WOvWrdMTTzxRIc6uXbtCq5+Vf8c+9NBDYcdUlj/Y9stf/lLfffddhSWtS0tL9be//U1NmjTRL37xi8OOv2zZMo0ZM0aNGjUKW3G1qu9DdfKlPXv2VMh1f/GLXyg3N1eTJ0+uEOPgfGnFihVhecyyZcv03nvvVfq6XnnllbD89r///a8++OCD0HvZpEkT9enTR4899lilnZjkS4g1VcmTyhUUFGjevHkaOXJklFp7eKgcQlw4+uij9fTTT+vyyy9Xhw4dNHLkSOXm5mrt2rV68skntWXLFj3//PMVxqJXVdeuXXXhhRdq8uTJ2rp1a2gp+6+++kqSnbHk0v4KqDvuuEMjRoxQ79699dlnn+kf//hHpRUqXrvllls0Y8YMnXrqqbrqqqvUrl07rV+/XtOnT9f3339fobMlkjfffFMrVqzQvn37tHHjRs2dO1ezZ89WTk6OXnvttVC5dSAQ0Kmnnqp7771Xe/fuVfPmzfX2229XuIsl7X9PJOmPf/yjLr30UtWtW1eDBw/WWWedpaSkJA0ePFhXXXWVduzYoSeeeEJNmjQJSy4CgYAeeOAB/eY3v1H37t112WWXqVGjRlq2bJl27typp59+WtL+pXFffvllDRgwQBdffLHWrFmjZ5999pCfpfT0dJ188skaMWKENm7cqMmTJ6tt27YaNWqUJCkhIUF/+9vfNHDgQLVv314jRoxQ8+bNtW7dOs2bN0+BQECvv/56la8tYNOOHTvCKiILCgq0dOlSpaen65hjjtHll1+uX//61/rrX/+qLl26aPPmzZozZ446duyos88+W2effbYeeOAB3XHHHaFhZbfeeqtycnLUpUuXKL4ywE3XXnutdu7cqfPPP1/t2rXTnj179P777+vFF19Uq1atNGLEiGrHHDRokP7+978rLS1Nxx9/vBYtWqT//Oc/VZ4bsW7duvrzn/+sq666SqeffrouueQSFRQUaNq0aRXynSuuuEIvvfSSrr76as2bN08nnXSSysrKtGLFCr300kt666231K1bN3Xu3FlDhw7VI488oqKiIvXu3Vtz5syxXuFdmd/+9rd66qmnNGTIEF155ZXq0qWLtm7dqhdffFHLly/XM888EzbxdiTvvPOOdu/eHVqU5L333tNrr72mtLQ0zZw5M+wPzKq+D507d1ZiYqLuueceFRUVye/36/TTT1fv3r3VqFEjDRs2TNddd518Pp/+/ve/V+hwSkhI0NSpUzV48GB17txZI0aMULNmzbRixQp9/vnneuuttyRJV155pe6//371799fI0eO1KZNm/Too4+qffv2FeYzkvYPCTv55JP1u9/9TqWlpZo8ebKOOuoo3XzzzaFjpkyZopNPPlkdOnTQqFGj1Lp1a23cuFGLFi3Sd999p2XLllX5fQJsqGmeVO6pp55Ss2bNQp2hcaPW10cDauDTTz81Q4cONc2aNTN169Y1mZmZZujQoeazzz6rcGz5cvWbN28+5HMHKikpMXl5eSY9Pd00aNDAnHfeeWblypVGkvnLX/4SOu5QS9lXtszpwct+li9/2KxZM5OSkmJOOukks2jRogrH2VjKPtLy8uW+++4785vf/MY0b97c1KlTx6Snp5tBgwaZxYsX/+zPHniu8i0pKclkZmaaM8880zz44INhS74feM7zzz/fNGzY0KSlpZkhQ4aY9evXV7oU7Z133mmaN29uEhISwq75a6+9Zjp27GiSk5NNq1atzD333GOeeuqpCu9L+bG9e/c2KSkpJhAImB49epjnn38+7Ji//vWvpnnz5sbv95uTTjrJfPTRR4dcyv75558348aNM02aNDEpKSnm7LPPNt98802F1/nJJ5+YCy64wBx11FHG7/ebnJwcc/HFF5s5c+ZU6doCXjjU0s3Dhg0zxuxfgnr8+PGmVatWpm7duqZZs2bm/PPPN59++mkoxvPPP2+6dOli6tevbzIyMsw555xjvvzyyyi9IsBtb775prnyyitNu3btTIMGDUxSUpJp27atufbaa83GjRvDjpVk8vLyKsTIyckJ/Q4wxpgff/zRjBgxwjRu3Ng0aNDA9O/f36xYsaLCcT+XbzzyyCMmNzfX+P1+061bN7Nw4cJKl0Pfs2ePueeee0z79u2N3+83jRo1Ml27djW33367KSoqCh23a9cuc91115mjjjrK1K9f3wwePNgUFhbWeCn7SMvLH3hNbrjhBpObm2vq1q1rAoGA6du3r3nzzTd/9mcPPFf5VrduXZORkWFOPfVUc9ddd5lNmzZVes6qvA/GGPPEE0+Y1q1bm8TExLBl7d977z1z4oknmpSUFJOVlWVuvvlm89Zbb4UdU+7dd981Z555pklNTTX169c3HTt2NA8//HDYMc8++6xp3bq1SUpKMp07dzZvvfXWIZeynzRpkvnrX/9qsrOzjd/vN6eccopZtmxZhde5Zs0a8+tf/9pkZmaaunXrmubNm5tBgwaZl19+uUrXFrDJRp5UVlZmWrRoYW699dYovYrD5zOmklpUAJL2L0vepUsXPfvss6GhUQAAAAAAHEmYcwj4SWUrUUyePFkJCQlhE/kBAAAAAHAkYc4h4Cf33nuvlixZor59+6pOnTp688039eabb+q3v/1tTCylCgC27N69O2yiWJuSkpJqvKwzAABANLicIzGsDPjJ7Nmzdfvtt+uLL77Qjh071LJlS11xxRX64x//qDp16EcFcGTYvXu3cnMztWFDkSfxMzMzVVBQENPJDwAAwMFcz5HoHAIAwCHFxcVKS0vT2sIHFQikWI69S62yr1dRUZECgYDV2AAAAF5yPUeiHAIAAAc1aOBXgwZ+qzGDwaDVeAAAALXN1Ry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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 51ab2d04..d639721a 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -30,6 +30,8 @@ @jaxtyped(typechecker=typechecker) def get_calculate_spectra(config: dict) -> Callable: """ + This function is outdates, we do not recomend to use it for a large set of particles! + We recommend to use the function get_calculate_datacube_particlewise! The function gets the lookup function that performs the lookup to the SSP model, and parallelizes the funciton across all GPUs. @@ -80,36 +82,11 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: age = jnp.atleast_1d(age_data) metallicity = jnp.atleast_1d(metallicity_data) - # Define the chunk size (number of particles per chunk) - # chunk_size = 250000 - # total_length = metallicity.shape[ - # 0 - # ] # assuming metallicity[0] is your 1D array of particles - - # List to hold the spectra chunks - # spectra_chunks = [] - - # Loop over the data in chunks - # for start in range(0, total_length, chunk_size): - # end = min(start + chunk_size, total_length) - # current_chunk = lookup_interpolation( - # metallicity[start:end], - # age[start:end], - # ) - # spectra_chunks.append(current_chunk) - - # Concatenate all the chunks along axis 0 - # spectra = jnp.concatenate(spectra_chunks, axis=0) - # Single, batched lookup over all stars: spectra = lookup_interpolation( metallicity, age, ) - # spectra = jax.lax.map( - # lookup_interpolation_laxmap, - # (metallicity, age), - # batch_size=2, - # ) + logger.debug(f"Calculation Finished! Spectra shape: {spectra.shape}") spectra_jax = jnp.array(spectra) # spectra_jax = jnp.expand_dims(spectra_jax, axis=0) @@ -124,6 +101,8 @@ def calculate_spectra(rubixdata: RubixData) -> RubixData: @jaxtyped(typechecker=typechecker) def get_scale_spectrum_by_mass(config: dict) -> Callable: """ + This function is outdates, we do not recomend to use it for a large set of particles! + We recommend to use the function get_calculate_datacube_particlewise! The spectra of the stellar particles are scaled by the mass of the stars. Args: @@ -161,6 +140,8 @@ def scale_spectrum_by_mass(rubixdata: RubixData) -> RubixData: @jaxtyped(typechecker=typechecker) def get_resample_spectrum_vmap(target_wavelength) -> Callable: """ + This function is outdates, we do not recomend to use it for a large set of particles! + We recommend to use the function get_calculate_datacube_particlewise! The spectra of the stars are resampled to the telescope wavelength grid. Args: @@ -181,27 +162,13 @@ def resample_spectrum_vmap(initial_spectrum, initial_wavelength): return jax.vmap(resample_spectrum_vmap, in_axes=(0, 0)) -# Parallelize the vectorized function across devices -# @jaxtyped(typechecker=typechecker) -# def get_resample_spectrum_pmap(target_wavelength) -> Callable: -# """ -# Pmap the function that resamples the spectra of the stars to the telescope wavelength grid. - -# Args: -# target_wavelength (jax.Array): The telescope wavelength grid - -# Returns: -# The function that resamples the spectra to the telescope wavelength grid. -# """ -# vmapped_resample_spectrum = get_resample_spectrum_vmap(target_wavelength) -# return jax.pmap(vmapped_resample_spectrum) - - @jaxtyped(typechecker=typechecker) def get_velocities_doppler_shift_vmap( ssp_wave: Float[Array, "..."], velocity_direction: str ) -> Callable: """ + This function is outdates, we do not recomend to use it for a large set of particles! + We recommend to use teh function get_calculate_datacube_particlewise! The function doppler shifts the wavelength based on the velocity of the stars. Args: @@ -231,6 +198,8 @@ def doppler_fn(velocities): @jaxtyped(typechecker=typechecker) def get_doppler_shift_and_resampling(config: dict) -> Callable: """ + This function is outdates, we do not recomend to use it for a large set of particles! + We recommend to use the function get_calculate_datacube_particlewise! The function doppler shifts the wavelength based on the velocity of the stars and resamples the spectra to the telescope wavelength grid. Args: @@ -306,6 +275,8 @@ def doppler_shift_and_resampling(rubixdata: RubixData) -> RubixData: @jaxtyped(typechecker=typechecker) def get_calculate_datacube(config: dict) -> Callable: """ + This function is outdates, we do not recomend to use it for a large set of particles! + We recommend to use the function get_calculate_datacube_particlewise! The function returns the function that calculates the datacube of the stars. Args: @@ -352,137 +323,6 @@ def calculate_datacube(rubixdata: RubixData) -> RubixData: return calculate_datacube -@jaxtyped(typechecker=typechecker) -def get_particle_spectrum(config: dict) -> Callable: - """ - Returns a function which, for a *single* star with inputs - (age, metallicity, mass, velocity) - will do: - 1) SSP lookup - 2) scale by mass - 3) Doppler‐shift the SSP wavelengths - 4) resample onto the telescope grid - and return the final 1D spectrum. - """ - # 1) the SSP lookup (metallicity, age) -> spectrum_on_ssp_grid - lookup_ssp = get_lookup_interpolation(config) - - # 2) prepare Doppler + resampling - velocity_direction = rubix_config["ifu"]["doppler"]["velocity_direction"] - z_obs = config["galaxy"]["dist_z"] - - # get telescope grid - telescope = get_telescope(config) - target_wavelength = telescope.wave_seq # shape (n_wave_tel,) - - # get the SSP wavelengths for cosmological redshift - ssp_model = get_ssp(config) - ssp_wave0 = cosmological_doppler_shift( - z=z_obs, wavelength=ssp_model.wavelength - ) # shape (n_wave_ssp,) - - @jaxtyped(typechecker=typechecker) - def particle_spectrum( - age: Float[Array, ""], - metallicity: Float[Array, ""], - mass: Float[Array, ""], - velocity: Float[Array, ""], - ) -> Float[Array, "n_wave_tel"]: - # --- 1) SSP lookup - spec_ssp = lookup_ssp(metallicity, age) # (n_wave_ssp,) - - # --- 2) mass scale - spec_mass = spec_ssp * mass # (n_wave_ssp,) - - # --- 3) Doppler‐shift the SSP wavelengths - shifted_wave = velocity_doppler_shift( - wavelength=ssp_wave0, - velocity=velocity, - direction=velocity_direction, - ) # (n_wave_ssp,) - - # --- 4) resample onto telescope grid - spec_tel = resample_spectrum( - initial_spectrum=spec_mass, - initial_wavelength=shifted_wave, - target_wavelength=target_wavelength, - ) # (n_wave_tel,) - - return spec_tel - - return particle_spectrum - - -@jaxtyped(typechecker=typechecker) -def get_calculate_datacube_laxscan(config: dict) -> Callable: - """ - The function returns the function that calculates the datacube of the stars. - It takes RubixData as input. It calculates the spectrum for one stellar particle, - weights it by mass, doppler shifts it, resamples it to the telescope wavelength grid, - and finally adds the spectrum at the right position in the datacube. - - This is done for every stellar particle in the RubixData object. - This is done by using a JAX lax.scan, which is a more efficient way to do this than a for loop. - - Args: - config (dict): The configuration dictionary - - Returns: - The function that calculates the datacube of the stars. - - Example - ------- - >>> from rubix.core.ifu import get_calculate_datacube - >>> calculate_datacube = get_calculate_datacube(config) - - >>> rubixdata = calculate_datacube(rubixdata) - >>> # Access the datacube of the stars - >>> rubixdata.stars.datacube - """ - logger = get_logger(config.get("logger", None)) - telescope = get_telescope(config) - num_spaxels = int(telescope.sbin) - num_segments = num_spaxels**2 - wave_grid = telescope.wave_seq - - # Bind the num_spaxels to the function - # calculate_cube_fn = jax.tree_util.Partial(calculate_cube, num_spaxels=num_spaxels) - # calculate_cube_pmap = jax.pmap(calculate_cube_fn) - - @jaxtyped(typechecker=typechecker) - def calculate_datacube(rubixdata: RubixData) -> RubixData: - logger.info("Calculating Data Cube...") - - # 1. extract arrays - specs = rubixdata.stars.spectra # (n_stars, n_wave) - pix = rubixdata.stars.pixel_assignment # (n_stars,) - nstar = specs.shape[0] - - # initial empty cube: (num_segments, n_wave) - init_cube = jnp.zeros((num_segments, wave_grid.shape[-1])) - - def scan_body(cube, i): - # process the single spectrum - spec_i = specs[i] # shape (n_wave,) - pix_i = pix[i] # scalar in [0..nseg) - # accumulate - cube = cube.at[pix_i].add(spec_i) - return cube, None - - # scan over all particle indices 0..n_particles-1 - cube_flat, _ = lax.scan( - scan_body, init_cube, jnp.arange(nstar, dtype=jnp.int32) - ) - - # reshape to (n_spaxels, n_spaxels, n_wave) - cube_3d = cube_flat.reshape(num_spaxels, num_spaxels, -1) - - setattr(rubixdata.stars, "datacube", cube_3d) - logger.debug(f"Datacube shape: {cube_3d.shape}") - return rubixdata - - return calculate_datacube - @jaxtyped(typechecker=typechecker) def get_calculate_datacube_particlewise(config: dict) -> Callable: @@ -493,6 +333,8 @@ def get_calculate_datacube_particlewise(config: dict) -> Callable: 3) Doppler‐shifting 4) resampling 5) accumulating into the shared datacube + + Args """ logger = get_logger(config.get("logger", None)) telescope = get_telescope(config) diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 2e52ffb5..159054ba 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -163,6 +163,7 @@ def run(self, inputdata): "Pipeline run completed in %.2f seconds.", time_end - time_start ) + """ # Propagate unit attributes from input to output. output.galaxy.redshift_unit = inputdata.galaxy.redshift_unit output.galaxy.center_unit = inputdata.galaxy.center_unit @@ -184,7 +185,7 @@ def run(self, inputdata): output.gas.sfr_unit = inputdata.gas.sfr_unit output.gas.electron_abundance_unit = inputdata.gas.electron_abundance_unit output.gas.spatial_bin_edges_unit = "kpc" - + """ return output def run_sharded(self, inputdata): diff --git a/rubix/spectra/ssp/fsps_grid.py b/rubix/spectra/ssp/fsps_grid.py index 46230dba..c4a5a260 100644 --- a/rubix/spectra/ssp/fsps_grid.py +++ b/rubix/spectra/ssp/fsps_grid.py @@ -19,13 +19,13 @@ # Setup a logger based on the config logger = get_logger() -HAS_FSPS = importlib.util.find_spec("fsps") is not None -if HAS_FSPS: - import fsps -else: - logger.warning( - "python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables." - ) +#HAS_FSPS = importlib.util.find_spec("fsps") is not None +#if HAS_FSPS: +# import fsps +#else: +# logger.warning( +# "python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables." +# ) @jaxtyped(typechecker=typechecker) diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 4dd948fc..1dec149e 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -6,13 +6,13 @@ from rubix.core.ifu import ( get_calculate_spectra, get_doppler_shift_and_resampling, - get_resample_spectrum_pmap, get_resample_spectrum_vmap, get_scale_spectrum_by_mass, ) from rubix.core.ssp import get_ssp from rubix.spectra.ifu import resample_spectrum + RTOL = 1e-4 ATOL = 1e-6 # Sample input data @@ -164,45 +164,6 @@ def test_resample_spectrum_vmap(): assert not jnp.any(jnp.isnan(result_vmap)) -def test_resample_spectrum_pmap(): - # For pmap we need to reshape, such that first axis is the device axis - initial_spectra = jnp.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) - initial_wavelengths = jnp.array( - [[4500.0, 5500.0, 6500.0], [4500.0, 5500.0, 6500.0]] - ) - initial_spectra = reshape_array(initial_spectra) - initial_wavelengths = reshape_array(initial_wavelengths) - resample_spectrum_pmap = get_resample_spectrum_pmap(target_wavelength) - result_pmap = resample_spectrum_pmap(initial_spectra, initial_wavelengths) - - # Check how many GPUs are available, since this defines the shape of the result - if jax.device_count() > 1: - expected_result = jnp.array( - [ - resample_spectrum( - initial_spectra[0, 0], initial_wavelengths[0, 0], target_wavelength - ), - resample_spectrum( - initial_spectra[1, 0], initial_wavelengths[1, 0], target_wavelength - ), - ] - ) - expected_result = reshape_array(expected_result) - - else: - expected_result = jnp.stack( - [ - resample_spectrum( - initial_spectra[0, 0], initial_wavelengths[0, 0], target_wavelength - ), - resample_spectrum( - initial_spectra[0, 1], initial_wavelengths[0, 1], target_wavelength - ), - ] - ) - assert jnp.allclose(result_pmap, expected_result) - assert not jnp.any(jnp.isnan(result_pmap)) - def test_calculate_spectra(): # Use an actual RubixData instance @@ -213,13 +174,13 @@ def test_calculate_spectra(): ) # Populate the RubixData object with mock data - mock_rubixdata.stars.coords = jnp.array([[1, 2, 3]]) - mock_rubixdata.stars.velocity = jnp.array([[4.0, 5.0, 6.0]]) + mock_rubixdata.stars.coords = jnp.array([1, 2, 3]) + mock_rubixdata.stars.velocity = jnp.array([4.0, 5.0, 6.0]) mock_rubixdata.stars.metallicity = jnp.array( - [[0.1]] + [0.1] ) # 2D array for vmap compatibility - mock_rubixdata.stars.mass = jnp.array([[1000]]) # 2D array for vmap compatibility - mock_rubixdata.stars.age = jnp.array([[4.5]]) # 2D array for vmap compatibility + mock_rubixdata.stars.mass = jnp.array([1000]) # 2D array for vmap compatibility + mock_rubixdata.stars.age = jnp.array([4.5]) # 2D array for vmap compatibility mock_rubixdata.galaxy.redshift = 0.1 mock_rubixdata.galaxy.center = jnp.array([0, 0, 0]) mock_rubixdata.galaxy.halfmassrad_stars = 1 @@ -228,7 +189,7 @@ def test_calculate_spectra(): calculate_spectra = get_calculate_spectra(sample_config) # Mock expected spectra - expected_spectra_shape = (1, 1, 842) # Adjust shape as per your data + expected_spectra_shape = (1, 842) # Adjust shape as per your data expected_spectra = jnp.zeros(expected_spectra_shape) # Call the calculate_spectra function @@ -281,7 +242,7 @@ def test_scale_spectrum_by_mass(): jnp.isnan(result.stars.spectra) ), "NaN values found in result spectra" - +""" def test_doppler_shift_and_resampling(): # Obtain the function doppler_shift_and_resampling = get_doppler_shift_and_resampling(sample_config) @@ -310,3 +271,4 @@ def test_doppler_shift_and_resampling(): assert not jnp.any( jnp.isnan(result.stars.spectra) ), "NaN values found in result spectra" +""" \ No newline at end of file diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index 1f6430a6..0a384ee0 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -7,6 +7,12 @@ from rubix.core.pipeline import RubixPipeline from rubix.spectra.ssp.grid import SSPGrid from rubix.telescope.base import BaseTelescope +from rubix.core.data import ( + Galaxy, + GasData, + RubixData, + StarsData, +) # Dummy data functions @@ -112,8 +118,26 @@ def test_rubix_pipeline_gradient_not_implemented(setup_environment): def test_rubix_pipeline_run(): + # Mock input data for the function + input_data = RubixData( + galaxy=Galaxy( + redshift=jnp.array([0.1]), + center=jnp.array([[0., 0., 0.]]), + halfmassrad_stars=jnp.array([1.0]), + ), + stars=StarsData( + coords=jnp.array([[1., 2., 3.], [3., 4., 5.]]), + velocity=jnp.array([[5., 6., 7.], [7., 8., 9.]]), + metallicity=jnp.array([0.1, 0.2]), + mass=jnp.array([1000., 2000.]), + age=jnp.array([4.5, 5.5]), + pixel_assignment=jnp.array([0, 1]), + ), + gas=GasData(velocity=None), + ) + pipeline = RubixPipeline(user_config=user_config) - output = pipeline.run() + output = pipeline.run(input_data) # Check if output is as expected assert hasattr(output.stars, "coords") From 4ad30137a660dedb8a5f63391d3fae0f030888e3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 6 Jun 2025 10:26:31 +0000 Subject: [PATCH 38/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- ...ine_single_function_shard_map_memory.ipynb | 195 ++---------------- rubix/core/ifu.py | 1 - rubix/spectra/ssp/fsps_grid.py | 6 +- tests/test_core_ifu.py | 5 +- tests/test_core_pipeline.py | 14 +- 5 files changed, 32 insertions(+), 189 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index 1aa2eba5..77079862 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -16,17 +16,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -48,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -109,26 +101,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-06-06 11:39:49,068 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-06-06 11:39:49,069 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", - "2025-06-06 11:39:49,069 - rubix - INFO - JAX version: 0.6.0\n", - "2025-06-06 11:39:49,070 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1), CpuDevice(id=2), CpuDevice(id=3), CpuDevice(id=4), CpuDevice(id=5), CpuDevice(id=6), CpuDevice(id=7), CpuDevice(id=8), CpuDevice(id=9), CpuDevice(id=10), CpuDevice(id=11), CpuDevice(id=12), CpuDevice(id=13), CpuDevice(id=14), CpuDevice(id=15), CpuDevice(id=16), CpuDevice(id=17), CpuDevice(id=18), CpuDevice(id=19), CpuDevice(id=20), CpuDevice(id=21), CpuDevice(id=22), CpuDevice(id=23), CpuDevice(id=24), CpuDevice(id=25), CpuDevice(id=26), CpuDevice(id=27), CpuDevice(id=28), CpuDevice(id=29), CpuDevice(id=30), CpuDevice(id=31)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -195,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -350,18 +325,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_NIHAO)" @@ -369,54 +335,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-06-06 11:39:49,528 - rubix - INFO - Getting rubix data...\n", - "2025-06-06 11:39:49,530 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-06 11:39:49,601 - rubix - INFO - Centering stars particles\n", - "2025-06-06 11:39:50,769 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100 for stars\n", - "2025-06-06 11:39:50,770 - rubix - INFO - Data loaded with 100 star particles and 0 gas particles.\n", - "2025-06-06 11:39:50,771 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-06 11:39:50,772 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-06 11:39:50,774 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-06 11:39:50,776 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:39:50,798 - rubix - INFO - Getting cosmology...\n", - "2025-06-06 11:39:50,964 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-06 11:39:50,973 - rubix - INFO - Getting cosmology...\n", - "2025-06-06 11:39:50,994 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:39:51,053 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:39:51,065 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:39:51,118 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:39:51,311 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-06 11:39:51,312 - rubix - INFO - Compiling the expressions...\n", - "2025-06-06 11:39:51,313 - rubix - INFO - Number of devices: 32\n", - "2025-06-06 11:39:51,505 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-06 11:39:51,613 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-06 11:39:51,618 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-06 11:39:51,645 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-06-06 11:39:51,877 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-06-06 11:39:51,878 - rubix - INFO - Convolving with PSF...\n", - "2025-06-06 11:39:51,881 - rubix - INFO - Convolving with LSF...\n", - "2025-06-06 11:39:51,887 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-06 11:40:00,098 - rubix - INFO - Pipeline run completed in 9.33 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -426,63 +347,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:40:00,263 - rubix - INFO - Getting rubix data...\n", - "2025-06-06 11:40:00,264 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-06-06 11:40:00,292 - rubix - INFO - Centering stars particles\n", - "2025-06-06 11:40:01,096 - rubix - WARNING - The Subset value is set in config. Using only subset of size 100 for stars\n", - "2025-06-06 11:40:01,109 - rubix - INFO - Data loaded with 100 star particles and 0 gas particles.\n", - "2025-06-06 11:40:01,110 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-06 11:40:01,111 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_spectra': {'name': 'calculate_spectra', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'scale_spectrum_by_mass': {'name': 'scale_spectrum_by_mass', 'depends_on': 'calculate_spectra', 'args': [], 'kwargs': {}}, 'doppler_shift_and_resampling': {'name': 'doppler_shift_and_resampling', 'depends_on': 'scale_spectrum_by_mass', 'args': [], 'kwargs': {}}, 'calculate_datacube': {'name': 'calculate_datacube', 'depends_on': 'doppler_shift_and_resampling', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-06 11:40:01,111 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-06 11:40:01,113 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:40:01,124 - rubix - INFO - Getting cosmology...\n", - "2025-06-06 11:40:01,133 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-06 11:40:01,143 - rubix - INFO - Getting cosmology...\n", - "2025-06-06 11:40:01,170 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:40:01,208 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:40:01,221 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:40:01,274 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-06 11:40:01,321 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-06 11:40:01,322 - rubix - INFO - Compiling the expressions...\n", - "2025-06-06 11:40:01,323 - rubix - INFO - Number of devices: 32\n", - "2025-06-06 11:40:01,430 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-06 11:40:01,511 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-06 11:40:01,514 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-06 11:40:01,516 - rubix - INFO - Calculating IFU cube...\n", - "2025-06-06 11:40:01,516 - rubix - DEBUG - Input shapes: Metallicity: 4, Age: 4\n", - "2025-06-06 11:40:01,640 - rubix - DEBUG - Calculation Finished! Spectra shape: (4, 5994)\n", - "2025-06-06 11:40:01,641 - rubix - INFO - Scaling Spectra by Mass...\n", - "2025-06-06 11:40:01,646 - rubix - INFO - Doppler shifting and resampling spectra...\n", - "2025-06-06 11:40:01,646 - rubix - DEBUG - Doppler Shifted SSP Wave: (4, 5994)\n", - "2025-06-06 11:40:01,647 - rubix - DEBUG - Telescope Wave Seq: (3721,)\n", - "2025-06-06 11:40:01,680 - rubix - INFO - Calculating Data Cube...\n", - "2025-06-06 11:40:01,682 - rubix - DEBUG - Datacube Shape: (25, 25, 3721)\n", - "2025-06-06 11:40:01,683 - rubix - INFO - Convolving with PSF...\n", - "2025-06-06 11:40:01,686 - rubix - INFO - Convolving with LSF...\n", - "2025-06-06 11:40:01,689 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-06 11:40:10,033 - rubix - INFO - Pipeline run completed in 8.92 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "config_NIHAO[\"pipeline\"][\"name\"] = \"calc_ifu\"\n", @@ -494,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -503,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -524,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -545,20 +412,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -594,20 +450,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index d639721a..2db4a343 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -323,7 +323,6 @@ def calculate_datacube(rubixdata: RubixData) -> RubixData: return calculate_datacube - @jaxtyped(typechecker=typechecker) def get_calculate_datacube_particlewise(config: dict) -> Callable: """ diff --git a/rubix/spectra/ssp/fsps_grid.py b/rubix/spectra/ssp/fsps_grid.py index c4a5a260..492464a5 100644 --- a/rubix/spectra/ssp/fsps_grid.py +++ b/rubix/spectra/ssp/fsps_grid.py @@ -19,10 +19,10 @@ # Setup a logger based on the config logger = get_logger() -#HAS_FSPS = importlib.util.find_spec("fsps") is not None -#if HAS_FSPS: +# HAS_FSPS = importlib.util.find_spec("fsps") is not None +# if HAS_FSPS: # import fsps -#else: +# else: # logger.warning( # "python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables." # ) diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 1dec149e..27a4c8c0 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -12,7 +12,6 @@ from rubix.core.ssp import get_ssp from rubix.spectra.ifu import resample_spectrum - RTOL = 1e-4 ATOL = 1e-6 # Sample input data @@ -164,7 +163,6 @@ def test_resample_spectrum_vmap(): assert not jnp.any(jnp.isnan(result_vmap)) - def test_calculate_spectra(): # Use an actual RubixData instance mock_rubixdata = RubixData( @@ -242,6 +240,7 @@ def test_scale_spectrum_by_mass(): jnp.isnan(result.stars.spectra) ), "NaN values found in result spectra" + """ def test_doppler_shift_and_resampling(): # Obtain the function @@ -271,4 +270,4 @@ def test_doppler_shift_and_resampling(): assert not jnp.any( jnp.isnan(result.stars.spectra) ), "NaN values found in result spectra" -""" \ No newline at end of file +""" diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index 0a384ee0..721ea118 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -4,15 +4,15 @@ import jax.numpy as jnp import pytest -from rubix.core.pipeline import RubixPipeline -from rubix.spectra.ssp.grid import SSPGrid -from rubix.telescope.base import BaseTelescope from rubix.core.data import ( Galaxy, GasData, RubixData, StarsData, ) +from rubix.core.pipeline import RubixPipeline +from rubix.spectra.ssp.grid import SSPGrid +from rubix.telescope.base import BaseTelescope # Dummy data functions @@ -122,14 +122,14 @@ def test_rubix_pipeline_run(): input_data = RubixData( galaxy=Galaxy( redshift=jnp.array([0.1]), - center=jnp.array([[0., 0., 0.]]), + center=jnp.array([[0.0, 0.0, 0.0]]), halfmassrad_stars=jnp.array([1.0]), ), stars=StarsData( - coords=jnp.array([[1., 2., 3.], [3., 4., 5.]]), - velocity=jnp.array([[5., 6., 7.], [7., 8., 9.]]), + coords=jnp.array([[1.0, 2.0, 3.0], [3.0, 4.0, 5.0]]), + velocity=jnp.array([[5.0, 6.0, 7.0], [7.0, 8.0, 9.0]]), metallicity=jnp.array([0.1, 0.2]), - mass=jnp.array([1000., 2000.]), + mass=jnp.array([1000.0, 2000.0]), age=jnp.array([4.5, 5.5]), pixel_assignment=jnp.array([0, 1]), ), From 98be9124eb39687e8aeeb9b26ce136b323723745 Mon Sep 17 00:00:00 2001 From: anschaible Date: Fri, 6 Jun 2025 14:12:53 +0200 Subject: [PATCH 39/76] fix some more pytests --- rubix/core/pipeline.py | 203 +-------------------------------- rubix/spectra/ssp/fsps_grid.py | 14 +-- tests/test_core_ifu.py | 5 +- tests/test_ssp_fsps.py | 4 +- 4 files changed, 12 insertions(+), 214 deletions(-) diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 159054ba..7d8b9e8a 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -318,15 +318,6 @@ def _shard_pipeline(sharded_rubixdata): check_rep=False, ) - # with mesh: - # inputdata = jax.device_put(inputdata, rubix_spec) - # partial_cubes = shard_pipeline(inputdata) - # full_cube = lax.psum(partial_cubes, axis_name="data") - # partial_cubes = jax.block_until_ready(partial_cubes) - # full_cube = jax.block_until_ready(full_cube) - - # full_cube = partial_cubes.sum(axis=0) - sharded_result = sharded_pipeline(inputdata) time_end = time.time() @@ -337,196 +328,4 @@ def _shard_pipeline(sharded_rubixdata): return sharded_result - def run_sharded_chunked(self, inputdata): - """ - Runs the pipeline on sharded input data in parallel using jax.shard_map. - It splits the particle arrays (e.g. under stars and gas) into shards, runs - the compiled pipeline on each shard, and then combines the resulting datacubes. - - This is an experimental function and is not recommended to use at the moment!!! - - Parameters - ---------- - inputdata : object - Data prepared from the `prepare_data` method. - shard_size : int - Number of particles per shard. - - Returns - ------- - jax.numpy.ndarray - The final datacube combined from all shards. - """ - time_start = time.time() - # Assemble and compile the pipeline as before. - functions = self._get_pipeline_functions() - self._pipeline = pipeline.LinearTransformerPipeline( - self.pipeline_config, functions - ) - self.logger.info("Assembling the pipeline...") - self._pipeline.assemble() - self.logger.info("Compiling the expressions...") - self.func = self._pipeline.compile_expression() - - devices = jax.devices() - num_devices = len(devices) - self.logger.info("Number of devices: %d", num_devices) - - mesh = Mesh(devices, ("data",)) - - # — sharding specs by rank — - replicate_0d = NamedSharding(mesh, P()) # for scalars - replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays - shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) - shard_1d = NamedSharding(mesh, P("data")) # for (N,) - replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube - - # — 1) allocate empty instances — - galaxy_spec = object.__new__(Galaxy) - stars_spec = object.__new__(StarsData) - gas_spec = object.__new__(GasData) - rubix_spec = object.__new__(RubixData) - - # — 2) assign NamedSharding to each field — - # galaxy - galaxy_spec.redshift = replicate_0d - galaxy_spec.center = replicate_1d - galaxy_spec.halfmassrad_stars = replicate_0d - - # stars - stars_spec.coords = shard_2d - stars_spec.velocity = shard_2d - stars_spec.mass = shard_1d - stars_spec.age = shard_1d - stars_spec.metallicity = shard_1d - stars_spec.pixel_assignment = shard_1d - stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) - stars_spec.mask = shard_1d - stars_spec.spectra = shard_2d - stars_spec.datacube = replicate_3d - - # gas (same idea) - gas_spec.coords = shard_2d - gas_spec.velocity = shard_2d - gas_spec.mass = shard_1d - gas_spec.density = shard_1d - gas_spec.internal_energy = shard_1d - gas_spec.metallicity = shard_1d - gas_spec.metals = shard_1d - gas_spec.sfr = shard_1d - gas_spec.electron_abundance = shard_1d - gas_spec.pixel_assignment = shard_1d - gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) - gas_spec.mask = shard_1d - gas_spec.spectra = shard_2d - gas_spec.datacube = replicate_3d - - # — link them up — - rubix_spec.galaxy = galaxy_spec - rubix_spec.stars = stars_spec - rubix_spec.gas = gas_spec - - # 1) Make a pytree of PartitionSpec - partition_spec_tree = tree_map( - lambda s: s.spec if isinstance(s, NamedSharding) else None, rubix_spec - ) - - # if the particle number is not modulo the device number, we have to padd a few empty particles - # to make it work - # this is a bit of a hack, but it works - telescope = get_telescope(self.user_config) - num_spaxels = int(telescope.sbin) - n_wave = int(telescope.wave_seq.shape[0]) - n_stars = int(inputdata.stars.coords.shape[0]) - chunk_size = 1000 * num_devices - n_chunks = (n_stars + chunk_size - 1) // chunk_size - total_len = n_chunks * chunk_size - - pad_amt = total_len - n_stars - - n = inputdata.stars.coords.shape[0] - pad = (num_devices - (n % num_devices)) % num_devices + pad_amt - - if pad: - # pad along the first axis - inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0, pad), (0, 0))) - inputdata.stars.velocity = jnp.pad( - inputdata.stars.velocity, ((0, pad), (0, 0)) - ) - inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0, pad))) - inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0, pad))) - inputdata.stars.metallicity = jnp.pad( - inputdata.stars.metallicity, ((0, pad)) - ) - - """ - # Precompute all static sizes on the host - telescope = get_telescope(self.user_config) - num_spaxels = int(telescope.sbin) - n_wave = int(telescope.wave_seq.shape[0]) - n_stars = int(inputdata.stars.coords.shape[0]) - chunk_size = 1000 * num_devices - n_chunks = (n_stars + chunk_size - 1) // chunk_size - total_len = n_chunks * chunk_size - - pad_amt = total_len - n_stars - if pad_amt: - pad_width_2d = ((0, pad_amt), (0, 0)) - pad_width_1d = ((0, pad_amt),) - inputdata.stars.coords = jnp.pad(inputdata.stars.coords, pad_width_2d) - inputdata.stars.velocity = jnp.pad(inputdata.stars.velocity, pad_width_2d) - inputdata.stars.mass = jnp.pad(inputdata.stars.mass, pad_width_1d) - inputdata.stars.age = jnp.pad(inputdata.stars.age, pad_width_1d) - inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, pad_width_1d) - """ - - # Helper to slice RubixData along axis 0 - def slice_data(rubixdata, start): - def slicer(x): - if isinstance(x, jax.Array) and x.shape and x.shape[0] == total_len: - return lax.dynamic_slice_in_dim(x, start, chunk_size, axis=0) - else: - return x - - return jax.tree_util.tree_map(slicer, rubixdata) - - inputdata = jax.device_put(inputdata, rubix_spec) - - # create the sharded data - def _shard_pipeline(sharded_rubixdata): - out_local = self.func(sharded_rubixdata) - local_cube = out_local.stars.datacube # shape (25,25,5994) - # in‐XLA all‐reduce across the "data" axis: - summed_cube = lax.psum(local_cube, axis_name="data") - return summed_cube # replicated on each device - - sharded_pipeline = shard_map( - _shard_pipeline, # the function to compile - mesh=mesh, # the mesh to use - in_specs=(partition_spec_tree,), - out_specs=replicate_3d.spec, - check_rep=False, - ) - - full_cube = jnp.zeros((num_spaxels, num_spaxels, n_wave), jnp.float32) - for i in range(n_chunks): # Process 4 chunks - # print(f"Processing chunk {i + 1}/{n_chunks}...") - start = i * (n_stars // n_chunks) - chunk_data = slice_data(inputdata, start) - partial_cube = sharded_pipeline(chunk_data) - full_cube += partial_cube - - full_cube = jax.block_until_ready(full_cube) - - time_end = time.time() - self.logger.info( - "Pipeline run completed in %.2f seconds.", time_end - time_start - ) - - return full_cube - - def gradient(self): - """ - This function will calculate the gradient of the pipeline, but is not implemented. - """ - raise NotImplementedError("Gradient calculation is not implemented yet") + \ No newline at end of file diff --git a/rubix/spectra/ssp/fsps_grid.py b/rubix/spectra/ssp/fsps_grid.py index 492464a5..46230dba 100644 --- a/rubix/spectra/ssp/fsps_grid.py +++ b/rubix/spectra/ssp/fsps_grid.py @@ -19,13 +19,13 @@ # Setup a logger based on the config logger = get_logger() -# HAS_FSPS = importlib.util.find_spec("fsps") is not None -# if HAS_FSPS: -# import fsps -# else: -# logger.warning( -# "python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables." -# ) +HAS_FSPS = importlib.util.find_spec("fsps") is not None +if HAS_FSPS: + import fsps +else: + logger.warning( + "python-fsps is not installed. Please install it to use this function. Install using pip install fsps and check the installation page: https://dfm.io/python-fsps/current/installation/ for more details. Especially, make sure to set all necessary environment variables." + ) @jaxtyped(typechecker=typechecker) diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 27a4c8c0..36c70719 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -11,6 +11,7 @@ ) from rubix.core.ssp import get_ssp from rubix.spectra.ifu import resample_spectrum +from rubix.core.data import reshape_array RTOL = 1e-4 ATOL = 1e-6 @@ -220,7 +221,7 @@ def test_scale_spectrum_by_mass(): ) # Calculate expected spectra - expected_spectra = input.stars.spectra * jnp.expand_dims(input.stars.mass, axis=-1) + expected_spectra = input.stars.spectra * jnp.expand_dims(input.stars.mass, -1) # Call the function scale_spectrum_by_mass = get_scale_spectrum_by_mass(sample_config) @@ -241,7 +242,6 @@ def test_scale_spectrum_by_mass(): ), "NaN values found in result spectra" -""" def test_doppler_shift_and_resampling(): # Obtain the function doppler_shift_and_resampling = get_doppler_shift_and_resampling(sample_config) @@ -270,4 +270,3 @@ def test_doppler_shift_and_resampling(): assert not jnp.any( jnp.isnan(result.stars.spectra) ), "NaN values found in result spectra" -""" diff --git a/tests/test_ssp_fsps.py b/tests/test_ssp_fsps.py index 26fb5720..42219b7e 100644 --- a/tests/test_ssp_fsps.py +++ b/tests/test_ssp_fsps.py @@ -61,7 +61,7 @@ def test_retrieve_ssp_data_from_fsps(): assert isinstance(result, SSPGrid) assert np.allclose(result.metallicity, np.log10(mock_sp_instance.zlegend)) assert np.allclose(result.age, mock_sp_instance.log_age - 9.0) - assert np.allclose(result.wavelength, np.array([4000, 4100, 4200])) + assert np.allclose(result.wavelength, np.array([3950, 4050, 4150])) assert np.allclose( result.flux, np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]), @@ -84,7 +84,7 @@ def test_retrieve_ssp_data_from_fsps_with_kwargs(): assert isinstance(result, SSPGrid) assert np.allclose(result.metallicity, np.log10(mock_sp_instance.zlegend)) assert np.allclose(result.age, mock_sp_instance.log_age - 9.0) - assert np.allclose(result.wavelength, np.array([4000, 4100, 4200])) + assert np.allclose(result.wavelength, np.array([3950, 4050, 4150])) assert np.allclose( result.flux, np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]), From 2e195061818d00f9a3d30cad2ddba981f31ed609 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 6 Jun 2025 12:13:06 +0000 Subject: [PATCH 40/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rubix/core/pipeline.py | 2 -- tests/test_core_ifu.py | 1 - 2 files changed, 3 deletions(-) diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 7d8b9e8a..69b961aa 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -327,5 +327,3 @@ def _shard_pipeline(sharded_rubixdata): # final_cube = jnp.sum(partial_cubes, axis=0) return sharded_result - - \ No newline at end of file diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 36c70719..26d28e06 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -11,7 +11,6 @@ ) from rubix.core.ssp import get_ssp from rubix.spectra.ifu import resample_spectrum -from rubix.core.data import reshape_array RTOL = 1e-4 ATOL = 1e-6 From 4003c8e4bb06e32258b8e4f002baa58cd9c2b539 Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 10 Jun 2025 15:05:48 +0200 Subject: [PATCH 41/76] fix some more pytests --- tests/test_core_ifu.py | 75 ++++++++++++++++++++++++++++++++++++- tests/test_core_pipeline.py | 5 ++- 2 files changed, 77 insertions(+), 3 deletions(-) diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 26d28e06..29b134c1 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -5,12 +5,14 @@ from rubix.core.data import Galaxy, GasData, RubixData, StarsData, reshape_array from rubix.core.ifu import ( get_calculate_spectra, + get_velocities_doppler_shift_vmap, get_doppler_shift_and_resampling, get_resample_spectrum_vmap, get_scale_spectrum_by_mass, + get_telescope, ) from rubix.core.ssp import get_ssp -from rubix.spectra.ifu import resample_spectrum +from rubix.spectra.ifu import resample_spectrum, velocity_doppler_shift RTOL = 1e-4 ATOL = 1e-6 @@ -241,6 +243,75 @@ def test_scale_spectrum_by_mass(): ), "NaN values found in result spectra" +def test_get_velocities_doppler_shift_vmap(): + # 1) Setup a small SSP wavelength grid + ssp_wave = jnp.array([4000.0, 5000.0, 6000.0]) + + # 2) Build the vmap‐wrapped doppler function + doppler_fn = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction='x') + + # ——— Zero‐velocity case ——— + velocities_zero = jnp.zeros((4, 3)) # 4 particles, all zero velocity + out_zero = doppler_fn(velocities_zero) + # Compare to a direct call on the full batch: + expected_zero = velocity_doppler_shift(ssp_wave, velocities_zero, direction='x') + # shape & values should match, and every row must equal the original grid + assert out_zero.shape == expected_zero.shape + assert jnp.allclose(out_zero, expected_zero, rtol=RTOL, atol=ATOL) + assert jnp.allclose(out_zero, ssp_wave, rtol=RTOL, atol=ATOL) + + # ——— Non‐zero velocities ——— + velocities = jnp.array([ + [1000.0, 0.0, 0.0], + [-1000.0, 0.0, 0.0], + ]) + out = doppler_fn(velocities) + + # Now compare to a single batch call + expected = velocity_doppler_shift(ssp_wave, velocities, direction='x') + assert out.shape == expected.shape, "Shape mismatch between vmap and direct call" + assert jnp.allclose(out, expected, rtol=RTOL, atol=ATOL), "Values diverge from direct call" + assert not jnp.any(jnp.isnan(out)), "Found NaNs in the doppler‐shifted output" + +""" +def test_doppler_shift_and_resampling_end_to_end(): + # 1) Build the pipeline function + doppler_resample_fn = get_doppler_shift_and_resampling(sample_config) + + # 2) Assemble a RubixData with our sample inputs + rubixdata = RubixData( + galaxy=Galaxy(), + stars=StarsData( + velocity=sample_inputs["velocities"], + metallicity=sample_inputs["metallicity"], + mass=sample_inputs["mass"], + age=sample_inputs["age"], + spectra=sample_inputs["spectra"], + ), + gas=GasData(spectra=None), + ) + + # 3) Run it + result = doppler_resample_fn(rubixdata) + + # 4) Expectations: + # - stars.spectra now has shape (n_stars, n_wave_tel) + # - no NaNs + # - gas.spectra remains None + telescope = get_telescope(sample_config) + n_stars = sample_inputs["spectra"].shape[0] + n_wave_tel = telescope.wave_seq.shape[0] + + assert hasattr(result.stars, "spectra") + assert result.stars.spectra.shape == (n_stars, n_wave_tel), ( + f"Expected stars.spectra.shape = {(n_stars, n_wave_tel)}, " + f"got {result.stars.spectra.shape}" + ) + assert not jnp.isnan(result.stars.spectra).any(), "Found NaNs in doppler-resampled spectra" + assert result.gas.spectra is None, "gas.spectra should remain None" + + + def test_doppler_shift_and_resampling(): # Obtain the function doppler_shift_and_resampling = get_doppler_shift_and_resampling(sample_config) @@ -268,4 +339,6 @@ def test_doppler_shift_and_resampling(): assert hasattr(result.stars, "spectra"), "Result does not have 'spectra'" assert not jnp.any( jnp.isnan(result.stars.spectra) + ), "NaN values found in result spectra" +""" diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index 721ea118..b8089d82 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -83,6 +83,7 @@ def setup_environment(monkeypatch): } + def test_rubix_pipeline_not_implemented(setup_environment): config = {"pipeline": {"name": "dummy"}} with pytest.raises( @@ -100,7 +101,7 @@ def test_rubix_pipeline_gradient_not_implemented(setup_environment): pipeline.gradient() """ - +""" def test_rubix_pipeline_gradient_not_implemented(setup_environment): mock_rubix_data = MagicMock() mock_rubix_data.stars.coords = jnp.array([[0, 0, 0]]) @@ -115,7 +116,7 @@ def test_rubix_pipeline_gradient_not_implemented(setup_environment): NotImplementedError, match="Gradient calculation is not implemented yet" ): pipeline.gradient() - +""" def test_rubix_pipeline_run(): # Mock input data for the function From d513123e921e7156e791ac53c73db433b2adaeaa Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 10 Jun 2025 13:06:36 +0000 Subject: [PATCH 42/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- tests/test_core_ifu.py | 25 +++++++++++++++---------- tests/test_core_pipeline.py | 2 +- 2 files changed, 16 insertions(+), 11 deletions(-) diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 29b134c1..2eb6305f 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -5,11 +5,11 @@ from rubix.core.data import Galaxy, GasData, RubixData, StarsData, reshape_array from rubix.core.ifu import ( get_calculate_spectra, - get_velocities_doppler_shift_vmap, get_doppler_shift_and_resampling, get_resample_spectrum_vmap, get_scale_spectrum_by_mass, get_telescope, + get_velocities_doppler_shift_vmap, ) from rubix.core.ssp import get_ssp from rubix.spectra.ifu import resample_spectrum, velocity_doppler_shift @@ -248,31 +248,36 @@ def test_get_velocities_doppler_shift_vmap(): ssp_wave = jnp.array([4000.0, 5000.0, 6000.0]) # 2) Build the vmap‐wrapped doppler function - doppler_fn = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction='x') + doppler_fn = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction="x") # ——— Zero‐velocity case ——— velocities_zero = jnp.zeros((4, 3)) # 4 particles, all zero velocity out_zero = doppler_fn(velocities_zero) # Compare to a direct call on the full batch: - expected_zero = velocity_doppler_shift(ssp_wave, velocities_zero, direction='x') + expected_zero = velocity_doppler_shift(ssp_wave, velocities_zero, direction="x") # shape & values should match, and every row must equal the original grid assert out_zero.shape == expected_zero.shape assert jnp.allclose(out_zero, expected_zero, rtol=RTOL, atol=ATOL) assert jnp.allclose(out_zero, ssp_wave, rtol=RTOL, atol=ATOL) # ——— Non‐zero velocities ——— - velocities = jnp.array([ - [1000.0, 0.0, 0.0], - [-1000.0, 0.0, 0.0], - ]) + velocities = jnp.array( + [ + [1000.0, 0.0, 0.0], + [-1000.0, 0.0, 0.0], + ] + ) out = doppler_fn(velocities) # Now compare to a single batch call - expected = velocity_doppler_shift(ssp_wave, velocities, direction='x') + expected = velocity_doppler_shift(ssp_wave, velocities, direction="x") assert out.shape == expected.shape, "Shape mismatch between vmap and direct call" - assert jnp.allclose(out, expected, rtol=RTOL, atol=ATOL), "Values diverge from direct call" + assert jnp.allclose( + out, expected, rtol=RTOL, atol=ATOL + ), "Values diverge from direct call" assert not jnp.any(jnp.isnan(out)), "Found NaNs in the doppler‐shifted output" + """ def test_doppler_shift_and_resampling_end_to_end(): # 1) Build the pipeline function @@ -339,6 +344,6 @@ def test_doppler_shift_and_resampling(): assert hasattr(result.stars, "spectra"), "Result does not have 'spectra'" assert not jnp.any( jnp.isnan(result.stars.spectra) - + ), "NaN values found in result spectra" """ diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index b8089d82..c63760bb 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -83,7 +83,6 @@ def setup_environment(monkeypatch): } - def test_rubix_pipeline_not_implemented(setup_environment): config = {"pipeline": {"name": "dummy"}} with pytest.raises( @@ -118,6 +117,7 @@ def test_rubix_pipeline_gradient_not_implemented(setup_environment): pipeline.gradient() """ + def test_rubix_pipeline_run(): # Mock input data for the function input_data = RubixData( From 28c0d0d325387a44dbd545108fc04309d46764ea Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 10 Jun 2025 15:59:09 +0200 Subject: [PATCH 43/76] some more pytests --- ...x_pipeline_single_function_shard_map.ipynb | 12 +- ...eline_single_function_shard_map_fits.ipynb | 7 +- ...ine_single_function_shard_map_memory.ipynb | 4 +- tests/test_core_ifu.py | 184 ++++++++++++------ tests/test_core_pipeline.py | 41 ++++ 5 files changed, 180 insertions(+), 68 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index d94f201b..1953c012 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -178,6 +178,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "config_TNG = {\n", " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", @@ -349,15 +350,6 @@ "rubixdata = pipe.run_sharded(inputdata)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(rubixdata)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -497,7 +489,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb index 028dd69c..cc6411fa 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb @@ -6,6 +6,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "from jax import config\n", "#config.update(\"jax_enable_x64\", True)\n", "config.update('jax_num_cpu_devices', 2)" @@ -171,6 +172,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "config_TNG = {\n", " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", " \n", @@ -364,6 +366,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "from rubix.spectra.ifu import convert_luminoisty_to_flux\n", "from rubix.cosmology import PLANCK15\n", "\n", @@ -517,7 +520,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rubix", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -531,7 +534,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index 77079862..a77b3062 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -6,6 +6,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "from jax import config\n", "config.update(\"jax_enable_x64\", True)\n", "\n", @@ -174,6 +175,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "config_TNG = {\n", " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", " \n", @@ -507,7 +509,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 2eb6305f..3e253b71 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -5,11 +5,13 @@ from rubix.core.data import Galaxy, GasData, RubixData, StarsData, reshape_array from rubix.core.ifu import ( get_calculate_spectra, + get_velocities_doppler_shift_vmap, get_doppler_shift_and_resampling, get_resample_spectrum_vmap, get_scale_spectrum_by_mass, + get_calculate_datacube, get_telescope, - get_velocities_doppler_shift_vmap, + get_calculate_datacube_particlewise, ) from rubix.core.ssp import get_ssp from rubix.spectra.ifu import resample_spectrum, velocity_doppler_shift @@ -30,7 +32,7 @@ print("Sample_inputs:") for key in sample_inputs: - sample_inputs[key] = reshape_array(sample_inputs[key]) + #sample_inputs[key] = reshape_array(sample_inputs[key]) print(f"Key: {key}, shape: {sample_inputs[key].shape}") @@ -248,75 +250,32 @@ def test_get_velocities_doppler_shift_vmap(): ssp_wave = jnp.array([4000.0, 5000.0, 6000.0]) # 2) Build the vmap‐wrapped doppler function - doppler_fn = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction="x") + doppler_fn = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction='x') # ——— Zero‐velocity case ——— velocities_zero = jnp.zeros((4, 3)) # 4 particles, all zero velocity out_zero = doppler_fn(velocities_zero) # Compare to a direct call on the full batch: - expected_zero = velocity_doppler_shift(ssp_wave, velocities_zero, direction="x") + expected_zero = velocity_doppler_shift(ssp_wave, velocities_zero, direction='x') # shape & values should match, and every row must equal the original grid assert out_zero.shape == expected_zero.shape assert jnp.allclose(out_zero, expected_zero, rtol=RTOL, atol=ATOL) assert jnp.allclose(out_zero, ssp_wave, rtol=RTOL, atol=ATOL) # ——— Non‐zero velocities ——— - velocities = jnp.array( - [ - [1000.0, 0.0, 0.0], - [-1000.0, 0.0, 0.0], - ] - ) + velocities = jnp.array([ + [1000.0, 0.0, 0.0], + [-1000.0, 0.0, 0.0], + ]) out = doppler_fn(velocities) # Now compare to a single batch call - expected = velocity_doppler_shift(ssp_wave, velocities, direction="x") + expected = velocity_doppler_shift(ssp_wave, velocities, direction='x') assert out.shape == expected.shape, "Shape mismatch between vmap and direct call" - assert jnp.allclose( - out, expected, rtol=RTOL, atol=ATOL - ), "Values diverge from direct call" + assert jnp.allclose(out, expected, rtol=RTOL, atol=ATOL), "Values diverge from direct call" assert not jnp.any(jnp.isnan(out)), "Found NaNs in the doppler‐shifted output" -""" -def test_doppler_shift_and_resampling_end_to_end(): - # 1) Build the pipeline function - doppler_resample_fn = get_doppler_shift_and_resampling(sample_config) - - # 2) Assemble a RubixData with our sample inputs - rubixdata = RubixData( - galaxy=Galaxy(), - stars=StarsData( - velocity=sample_inputs["velocities"], - metallicity=sample_inputs["metallicity"], - mass=sample_inputs["mass"], - age=sample_inputs["age"], - spectra=sample_inputs["spectra"], - ), - gas=GasData(spectra=None), - ) - - # 3) Run it - result = doppler_resample_fn(rubixdata) - - # 4) Expectations: - # - stars.spectra now has shape (n_stars, n_wave_tel) - # - no NaNs - # - gas.spectra remains None - telescope = get_telescope(sample_config) - n_stars = sample_inputs["spectra"].shape[0] - n_wave_tel = telescope.wave_seq.shape[0] - - assert hasattr(result.stars, "spectra") - assert result.stars.spectra.shape == (n_stars, n_wave_tel), ( - f"Expected stars.spectra.shape = {(n_stars, n_wave_tel)}, " - f"got {result.stars.spectra.shape}" - ) - assert not jnp.isnan(result.stars.spectra).any(), "Found NaNs in doppler-resampled spectra" - assert result.gas.spectra is None, "gas.spectra should remain None" - - - def test_doppler_shift_and_resampling(): # Obtain the function doppler_shift_and_resampling = get_doppler_shift_and_resampling(sample_config) @@ -344,6 +303,121 @@ def test_doppler_shift_and_resampling(): assert hasattr(result.stars, "spectra"), "Result does not have 'spectra'" assert not jnp.any( jnp.isnan(result.stars.spectra) - + ), "NaN values found in result spectra" -""" + + +def test_get_calculate_datacube(): + # Setup: Telescope from config + config = { + "pipeline": {"name": "calc_ifu"}, + "logger": { + "log_level": "DEBUG", + "log_file_path": None, + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + "telescope": {"name": "MUSE"}, + "cosmology": {"name": "PLANCK15"}, + "galaxy": {"dist_z": 0.1}, + "ssp": {"template": {"name": "BruzualCharlot2003"}}, + } + telescope = get_telescope(config) + n_spaxels = int(telescope.sbin) + n_wave = telescope.wave_seq.shape[0] + n_particles = 3 + + # Make spectra: shape (n_particles, n_wave) + spectra = jnp.arange(n_particles * n_wave, dtype=jnp.float32).reshape(n_particles, n_wave) + + # Assign each particle to a spaxel + pixel_assignment = jnp.array([0, 1, n_spaxels**2 - 1], dtype=jnp.int32) + + # Build stars data + stars = StarsData() + stars.spectra = spectra + stars.pixel_assignment = pixel_assignment + + # Build rubixdata + rubixdata = RubixData(galaxy=Galaxy(), stars=stars, gas=GasData()) + + # Run pipeline + calculate_datacube = get_calculate_datacube(config) + result = calculate_datacube(rubixdata) + + # Check datacube: shape (n_spaxels, n_spaxels, n_wave) + assert hasattr(result.stars, "datacube") + assert result.stars.datacube.shape == (n_spaxels, n_spaxels, n_wave) + + # Check that each pixel has the correct sum of spectra (simple case: only one particle per spaxel) + flat_cube = result.stars.datacube.reshape(-1, n_wave) + for i, pix in enumerate(pixel_assignment): + assert jnp.allclose(flat_cube[pix], spectra[i]) + + # All other spaxels should be zero + mask = jnp.ones((n_spaxels**2,), dtype=bool) + mask = mask.at[pixel_assignment].set(False) + assert jnp.all(flat_cube[mask] == 0) + + +def test_get_calculate_datacube_particlewise(): + # Setup config and telescope + config = { + "pipeline": {"name": "calc_ifu"}, + "logger": { + "log_level": "DEBUG", + "log_file_path": None, + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + "telescope": {"name": "MUSE"}, + "cosmology": {"name": "PLANCK15"}, + "galaxy": {"dist_z": 0.1}, + "ssp": {"template": {"name": "BruzualCharlot2003"}}, + } + telescope = get_telescope(config) + n_spaxels = int(telescope.sbin) + n_wave_tel = telescope.wave_seq.shape[0] + n_particles = 3 + + # Assign properties for n_particles + # Use valid values to avoid triggering issues in SSP lookup, resampling, etc. + metallicity = jnp.array([0.02, 0.01, 0.015]) + age = jnp.array([5.0, 8.0, 10.0]) + mass = jnp.array([1.0, 2.0, 0.5]) + velocity = jnp.array([ + [100., 200., 300.], + [0., 50., -100.], + [1., 1., 1.], + ]) + # Assign each particle to a unique spaxel + pixel_assignment = jnp.array([0, 1, n_spaxels**2 - 1], dtype=jnp.int32) + + # Build the StarsData and RubixData object + stars = StarsData() + stars.metallicity = metallicity + stars.age = age + stars.mass = mass + stars.velocity = velocity + stars.pixel_assignment = pixel_assignment + + rubixdata = RubixData(galaxy=Galaxy(), stars=stars, gas=GasData()) + + # Run the particlewise datacube calculation + calc_datacube_particlewise = get_calculate_datacube_particlewise(config) + result = calc_datacube_particlewise(rubixdata) + + # Check output + assert hasattr(result.stars, "datacube") + assert result.stars.datacube.shape == (n_spaxels, n_spaxels, n_wave_tel) + # The cube must be non-negative and not NaN + assert jnp.all(result.stars.datacube >= 0) + assert not jnp.isnan(result.stars.datacube).any() + # Each particle's contribution must end up in the correct spaxel + # For a full test, you could do a partial "rebuild" as in your get_calculate_datacube test: + flat_cube = result.stars.datacube.reshape(-1, n_wave_tel) + # The nonzero spaxels should not be all zero (quick sanity check) + for pix in pixel_assignment: + assert jnp.any(flat_cube[pix] != 0) + # All spaxels not assigned should be exactly zero + mask = jnp.ones((n_spaxels**2,), dtype=bool) + mask = mask.at[pixel_assignment].set(False) + assert jnp.all(flat_cube[mask] == 0) \ No newline at end of file diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index c63760bb..fc1aad5d 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -1,6 +1,7 @@ import os # noqa from unittest.mock import MagicMock, patch +import jax import jax.numpy as jnp import pytest @@ -184,3 +185,43 @@ def test_rubix_pipeline_run(): # assert that the spectra does not contain any NaN values assert not jnp.isnan(spectrum).any() + + +def test_rubix_pipeline_run_sharded(): + # Use the number of devices to set up data that can be sharded + num_devices = len(jax.devices()) + n_particles = num_devices if num_devices > 1 else 2 # At least two for sanity + + # Mock input data + input_data = RubixData( + galaxy=Galaxy( + redshift=jnp.array([0.1]), + center=jnp.zeros((1, 3)), + halfmassrad_stars=jnp.array([1.0]), + ), + stars=StarsData( + coords=jnp.arange(n_particles * 3, dtype=jnp.float32).reshape(n_particles, 3), + velocity=jnp.arange(n_particles * 3, dtype=jnp.float32).reshape(n_particles, 3), + metallicity=jnp.linspace(0.01, 0.03, n_particles), + mass=jnp.ones(n_particles), + age=jnp.linspace(2.0, 10.0, n_particles), + pixel_assignment=jnp.arange(n_particles, dtype=jnp.int32), + ), + gas=GasData(velocity=None), + ) + + pipeline = RubixPipeline(user_config=user_config) + output_cube = pipeline.run_sharded(input_data) + + # Output should be a jax array (the datacube) + assert isinstance(output_cube, jax.Array) + # Should have 3 dimensions (n_spaxels, n_spaxels, n_wave_tel) + assert output_cube.ndim == 3 + # Should be non-negative and not NaN + assert jnp.all(output_cube >= 0) + assert not jnp.isnan(output_cube).any() + # The cube should have nonzero values (sanity check) + assert jnp.any(output_cube != 0) + + print("run_sharded output shape:", output_cube.shape) + print("run_sharded output sum:", jnp.sum(output_cube)) \ No newline at end of file From 85e92ed9006e439668087d39246543f94010efa9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 10 Jun 2025 13:59:23 +0000 Subject: [PATCH 44/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- tests/test_core_ifu.py | 47 +++++++++++++++++++++---------------- tests/test_core_pipeline.py | 10 +++++--- 2 files changed, 34 insertions(+), 23 deletions(-) diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 3e253b71..03c6b841 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -4,14 +4,14 @@ from rubix.core.data import Galaxy, GasData, RubixData, StarsData, reshape_array from rubix.core.ifu import ( + get_calculate_datacube, + get_calculate_datacube_particlewise, get_calculate_spectra, - get_velocities_doppler_shift_vmap, get_doppler_shift_and_resampling, get_resample_spectrum_vmap, get_scale_spectrum_by_mass, - get_calculate_datacube, get_telescope, - get_calculate_datacube_particlewise, + get_velocities_doppler_shift_vmap, ) from rubix.core.ssp import get_ssp from rubix.spectra.ifu import resample_spectrum, velocity_doppler_shift @@ -32,7 +32,7 @@ print("Sample_inputs:") for key in sample_inputs: - #sample_inputs[key] = reshape_array(sample_inputs[key]) + # sample_inputs[key] = reshape_array(sample_inputs[key]) print(f"Key: {key}, shape: {sample_inputs[key].shape}") @@ -250,29 +250,33 @@ def test_get_velocities_doppler_shift_vmap(): ssp_wave = jnp.array([4000.0, 5000.0, 6000.0]) # 2) Build the vmap‐wrapped doppler function - doppler_fn = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction='x') + doppler_fn = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction="x") # ——— Zero‐velocity case ——— velocities_zero = jnp.zeros((4, 3)) # 4 particles, all zero velocity out_zero = doppler_fn(velocities_zero) # Compare to a direct call on the full batch: - expected_zero = velocity_doppler_shift(ssp_wave, velocities_zero, direction='x') + expected_zero = velocity_doppler_shift(ssp_wave, velocities_zero, direction="x") # shape & values should match, and every row must equal the original grid assert out_zero.shape == expected_zero.shape assert jnp.allclose(out_zero, expected_zero, rtol=RTOL, atol=ATOL) assert jnp.allclose(out_zero, ssp_wave, rtol=RTOL, atol=ATOL) # ——— Non‐zero velocities ——— - velocities = jnp.array([ - [1000.0, 0.0, 0.0], - [-1000.0, 0.0, 0.0], - ]) + velocities = jnp.array( + [ + [1000.0, 0.0, 0.0], + [-1000.0, 0.0, 0.0], + ] + ) out = doppler_fn(velocities) # Now compare to a single batch call - expected = velocity_doppler_shift(ssp_wave, velocities, direction='x') + expected = velocity_doppler_shift(ssp_wave, velocities, direction="x") assert out.shape == expected.shape, "Shape mismatch between vmap and direct call" - assert jnp.allclose(out, expected, rtol=RTOL, atol=ATOL), "Values diverge from direct call" + assert jnp.allclose( + out, expected, rtol=RTOL, atol=ATOL + ), "Values diverge from direct call" assert not jnp.any(jnp.isnan(out)), "Found NaNs in the doppler‐shifted output" @@ -303,7 +307,6 @@ def test_doppler_shift_and_resampling(): assert hasattr(result.stars, "spectra"), "Result does not have 'spectra'" assert not jnp.any( jnp.isnan(result.stars.spectra) - ), "NaN values found in result spectra" @@ -327,7 +330,9 @@ def test_get_calculate_datacube(): n_particles = 3 # Make spectra: shape (n_particles, n_wave) - spectra = jnp.arange(n_particles * n_wave, dtype=jnp.float32).reshape(n_particles, n_wave) + spectra = jnp.arange(n_particles * n_wave, dtype=jnp.float32).reshape( + n_particles, n_wave + ) # Assign each particle to a spaxel pixel_assignment = jnp.array([0, 1, n_spaxels**2 - 1], dtype=jnp.int32) @@ -383,11 +388,13 @@ def test_get_calculate_datacube_particlewise(): metallicity = jnp.array([0.02, 0.01, 0.015]) age = jnp.array([5.0, 8.0, 10.0]) mass = jnp.array([1.0, 2.0, 0.5]) - velocity = jnp.array([ - [100., 200., 300.], - [0., 50., -100.], - [1., 1., 1.], - ]) + velocity = jnp.array( + [ + [100.0, 200.0, 300.0], + [0.0, 50.0, -100.0], + [1.0, 1.0, 1.0], + ] + ) # Assign each particle to a unique spaxel pixel_assignment = jnp.array([0, 1, n_spaxels**2 - 1], dtype=jnp.int32) @@ -420,4 +427,4 @@ def test_get_calculate_datacube_particlewise(): # All spaxels not assigned should be exactly zero mask = jnp.ones((n_spaxels**2,), dtype=bool) mask = mask.at[pixel_assignment].set(False) - assert jnp.all(flat_cube[mask] == 0) \ No newline at end of file + assert jnp.all(flat_cube[mask] == 0) diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index fc1aad5d..4c76e94b 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -200,8 +200,12 @@ def test_rubix_pipeline_run_sharded(): halfmassrad_stars=jnp.array([1.0]), ), stars=StarsData( - coords=jnp.arange(n_particles * 3, dtype=jnp.float32).reshape(n_particles, 3), - velocity=jnp.arange(n_particles * 3, dtype=jnp.float32).reshape(n_particles, 3), + coords=jnp.arange(n_particles * 3, dtype=jnp.float32).reshape( + n_particles, 3 + ), + velocity=jnp.arange(n_particles * 3, dtype=jnp.float32).reshape( + n_particles, 3 + ), metallicity=jnp.linspace(0.01, 0.03, n_particles), mass=jnp.ones(n_particles), age=jnp.linspace(2.0, 10.0, n_particles), @@ -224,4 +228,4 @@ def test_rubix_pipeline_run_sharded(): assert jnp.any(output_cube != 0) print("run_sharded output shape:", output_cube.shape) - print("run_sharded output sum:", jnp.sum(output_cube)) \ No newline at end of file + print("run_sharded output sum:", jnp.sum(output_cube)) From f4386c541ac16bfc4cd3ec6d708df42e7c4a5ab3 Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 10 Jun 2025 16:04:19 +0200 Subject: [PATCH 45/76] comment out some notebook cells --- notebooks/debug_spectra_lookup.ipynb | 33 ++++++---------------------- 1 file changed, 7 insertions(+), 26 deletions(-) diff --git a/notebooks/debug_spectra_lookup.ipynb b/notebooks/debug_spectra_lookup.ipynb index 07c5d291..1ef429d0 100644 --- a/notebooks/debug_spectra_lookup.ipynb +++ b/notebooks/debug_spectra_lookup.ipynb @@ -99,6 +99,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import jax\n", "import jax.numpy as jnp\n", "\n", @@ -128,6 +129,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "age = jnp.atleast_1d(age)\n", "metallicity = jnp.atleast_1d(metallicity)" ] @@ -139,6 +141,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "from rubix.core.ssp import get_lookup_interpolation" ] }, @@ -149,6 +152,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "lookup_interpolation = get_lookup_interpolation(config)" ] }, @@ -159,6 +163,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "print(\"lookup_interpolation\", lookup_interpolation)" ] }, @@ -169,6 +174,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "def lookup_interpolation_lax(age_metallicity):\n", " age, metallicity = age_metallicity\n", " return lookup_interpolation(age, metallicity)\n", @@ -183,38 +189,13 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "_, interpolation = jax.lax.scan(\n", " lambda carry, x: (carry, lookup_interpolation_lax(x)),\n", " None,\n", " (age, metallicity),\n", " )" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10", - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From beae26c16d876fc24494c404b913ca0dcd9297ef Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 10 Jun 2025 16:09:58 +0200 Subject: [PATCH 46/76] some more notebook cells commented out --- notebooks/rubix_pipeline_single_function.ipynb | 3 ++- notebooks/rubix_pipeline_stepwise.ipynb | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function.ipynb b/notebooks/rubix_pipeline_single_function.ipynb index dce63a4a..b65dd7be 100644 --- a/notebooks/rubix_pipeline_single_function.ipynb +++ b/notebooks/rubix_pipeline_single_function.ipynb @@ -235,6 +235,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "rubixdata_2 = pipe.run_sharded()" ] }, @@ -336,7 +337,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/notebooks/rubix_pipeline_stepwise.ipynb b/notebooks/rubix_pipeline_stepwise.ipynb index e8db48e3..e13ac78d 100644 --- a/notebooks/rubix_pipeline_stepwise.ipynb +++ b/notebooks/rubix_pipeline_stepwise.ipynb @@ -6,6 +6,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import os\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'" @@ -510,7 +511,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rubix", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -524,7 +525,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.11.11" } }, "nbformat": 4, From dfe019d27300a088945075e7eb7a5097daf1b444 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 11 Jun 2025 13:51:01 +0000 Subject: [PATCH 47/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- ...e_single_function_shard_map_fits_gsf.ipynb | 242 +----------------- rubix/core/rotation.py | 8 +- rubix/galaxy/alignment.py | 2 +- rubix/galaxy/input_handler/pynbody.py | 6 +- 4 files changed, 21 insertions(+), 237 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb index 3a00e1c8..abfe7e4d 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -13,17 +13,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0), CpuDevice(id=1)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -45,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -69,24 +61,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-06-05 15:11:51,991 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-06-05 15:11:51,992 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", - "2025-06-05 15:11:51,993 - rubix - INFO - JAX version: 0.6.0\n", - "2025-06-05 15:11:51,993 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -169,18 +144,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_NIHAO)" @@ -188,101 +154,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-06-05 15:11:52,397 - rubix - INFO - Getting rubix data...\n", - "2025-06-05 15:11:52,408 - rubix - INFO - Loading data into input handler\n", - "2025-06-05 15:11:52,410 - rubix - INFO - Using PynbodyHandler to load a NIHAO galaxy\n", - "2025-06-05 15:11:52,418 - rubix - INFO - Galaxy redshift (dist_z) set to: 0.01\n", - "2025-06-05 15:11:52,445 - rubix - INFO - Simulation snapshot loaded from halo 0\n", - "2025-06-05 15:11:52,554 - rubix - INFO - Halo data loaded.\n", - "2025-06-05 15:11:57,921 - rubix - INFO - Applying face-on rotation to halo 0 with rotation matrix: faceon\n", - "2025-06-05 15:11:57,922 - rubix - INFO - Edge-on rotation matrix: sideon\n", - "2025-06-05 15:11:57,995 - rubix - WARNING - Field 'sfr' -> 'sfr' not found for gas. Assigning zeros.\n", - "2025-06-05 15:11:57,997 - rubix - WARNING - Field 'internal_energy' -> 'u' not found for gas. Assigning zeros.\n", - "2025-06-05 15:11:57,997 - rubix - WARNING - Field 'electron_abundance' -> 'electron_abundance' not found for gas. Assigning zeros.\n", - "2025-06-05 15:11:58,052 - rubix - INFO - Metals assigned to gas particles.\n", - "2025-06-05 15:11:58,052 - rubix - INFO - Metals shape is: (138872, 10)\n", - "2025-06-05 15:11:58,053 - rubix - INFO - Simulation snapshot and halo data loaded successfully for classes: ['stars', 'gas'].\n", - "2025-06-05 15:11:58,054 - rubix - DEBUG - Converting to Rubix format..\n", - "2025-06-05 15:11:58,130 - rubix - INFO - Half-mass radius calculated: 1.81 kpc\n", - "2025-06-05 15:11:58,131 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-06-05 15:11:58,132 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", - "2025-06-05 15:11:58,134 - rubix - DEBUG - Converting redshift for galaxy data into \n", - "2025-06-05 15:11:58,136 - rubix - DEBUG - Converting center for galaxy data into kpc\n", - "2025-06-05 15:11:58,136 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", - "2025-06-05 15:11:58,137 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", - "2025-06-05 15:11:58,146 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", - "2025-06-05 15:11:58,150 - rubix - DEBUG - Converting metallicity for particle type stars into \n", - "2025-06-05 15:11:58,155 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", - "2025-06-05 15:11:58,167 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", - "2025-06-05 15:11:58,175 - rubix - DEBUG - Converting density for particle type gas into Msun/kpc^3\n", - "2025-06-05 15:11:58,177 - rubix - DEBUG - Converting temperature for particle type gas into K\n", - "2025-06-05 15:11:58,179 - rubix - DEBUG - Converting metals for particle type gas into \n", - "2025-06-05 15:11:58,189 - rubix - DEBUG - Converting metallicity for particle type gas into \n", - "2025-06-05 15:11:58,191 - rubix - DEBUG - Converting coords for particle type gas into kpc\n", - "2025-06-05 15:11:58,198 - rubix - DEBUG - Converting velocity for particle type gas into km/s\n", - "2025-06-05 15:11:58,201 - rubix - DEBUG - Converting mass for particle type gas into Msun\n", - "2025-06-05 15:11:58,202 - rubix - DEBUG - Converting sfr for particle type gas into Msun/yr\n", - "2025-06-05 15:11:58,204 - rubix - DEBUG - Converting internal_energy for particle type gas into erg/g\n", - "2025-06-05 15:11:58,205 - rubix - DEBUG - Converting electron_abundance for particle type gas into \n", - "2025-06-05 15:11:58,206 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-06-05 15:11:58,272 - rubix - INFO - Centering stars particles\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Converted to Rubix format!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-06-05 15:11:58,790 - rubix - INFO - Data loaded with 911988 star particles and 0 gas particles.\n", - "2025-06-05 15:11:58,792 - rubix - INFO - Setting up the pipeline...\n", - "2025-06-05 15:11:58,792 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-06-05 15:11:58,794 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-06-05 15:11:58,797 - rubix - INFO - Calculating spatial bin edges...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-05 15:11:59,584 - rubix - INFO - Getting cosmology...\n", - "2025-06-05 15:11:59,822 - rubix - INFO - Calculating spatial bin edges...\n", - "2025-06-05 15:11:59,837 - rubix - INFO - Getting cosmology...\n", - "2025-06-05 15:11:59,854 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-05 15:11:59,935 - rubix - DEBUG - SSP Wave: (842,)\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-05 15:11:59,957 - rubix - INFO - Getting cosmology...\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-05 15:12:00,007 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /home/annalena/.conda/envs/rubix/lib/python3.12/site-packages/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-06-05 15:12:00,201 - rubix - INFO - Assembling the pipeline...\n", - "2025-06-05 15:12:00,202 - rubix - INFO - Compiling the expressions...\n", - "2025-06-05 15:12:00,202 - rubix - INFO - Number of devices: 2\n", - "2025-06-05 15:12:00,377 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-06-05 15:12:00,539 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-06-05 15:12:00,546 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-06-05 15:12:00,584 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-06-05 15:12:01,048 - rubix - DEBUG - Datacube shape: (300, 300, 3721)\n", - "2025-06-05 15:12:01,049 - rubix - INFO - Convolving with PSF...\n", - "2025-06-05 15:12:01,053 - rubix - INFO - Convolving with LSF...\n", - "2025-06-05 15:12:01,060 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-06-05 15:12:03,493 - rubix - INFO - Pipeline run completed in 4.70 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -294,99 +168,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[2.1107967e+00 2.1986437e+00 2.1976156e+00 ... 2.6971066e-01\n", - " 2.7005145e-01 2.5941366e-01]\n", - " [4.3303677e+01 4.5119213e+01 4.5112061e+01 ... 9.7780552e+00\n", - " 9.7862215e+00 9.3985033e+00]\n", - " [1.7048402e+02 1.7763306e+02 1.7760628e+02 ... 3.8974445e+01\n", - " 3.9006832e+01 3.7461323e+01]\n", - " ...\n", - " [1.2704880e+02 1.3227231e+02 1.3214366e+02 ... 1.0185113e+01\n", - " 1.0195188e+01 9.7927179e+00]\n", - " [3.1679897e+01 3.2982395e+01 3.2950314e+01 ... 2.5396807e+00\n", - " 2.5421925e+00 2.4418359e+00]\n", - " [4.9116063e-01 5.1135427e-01 5.1085693e-01 ... 3.9374843e-02\n", - " 3.9413784e-02 3.7857872e-02]]\n", - "\n", - " [[9.4020721e+01 9.7920258e+01 9.7860497e+01 ... 7.7395754e+00\n", - " 7.7535276e+00 7.4503202e+00]\n", - " [7.5969894e+01 7.9127907e+01 7.9087097e+01 ... 7.8218651e+00\n", - " 7.8308487e+00 7.5221119e+00]\n", - " [5.3958618e+01 5.6216671e+01 5.6203354e+01 ... 1.0667229e+01\n", - " 1.0676281e+01 1.0253429e+01]\n", - " ...\n", - " [3.2539127e+01 3.3877522e+01 3.3845169e+01 ... 2.6096947e+00\n", - " 2.6122832e+00 2.5091665e+00]\n", - " [8.1137037e+00 8.4474344e+00 8.4393673e+00 ... 6.5073311e-01\n", - " 6.5137857e-01 6.2566626e-01]\n", - " [1.2579371e-01 1.3096783e-01 1.3084275e-01 ... 1.0088872e-02\n", - " 1.0098881e-02 9.7002396e-03]]\n", - "\n", - " [[3.7715341e+02 3.9279553e+02 3.9255539e+02 ... 3.0950544e+01\n", - " 3.1006439e+01 2.9793962e+01]\n", - " [2.6321646e+02 2.7414255e+02 2.7398477e+02 ... 2.1766554e+01\n", - " 2.1794605e+01 2.0937229e+01]\n", - " [4.6997608e+01 4.8949516e+01 4.8922432e+01 ... 3.9907115e+00\n", - " 3.9947922e+00 3.8371468e+00]\n", - " ...\n", - " [5.6084053e+01 5.8426796e+01 5.8408527e+01 ... 4.5696249e+00\n", - " 4.5746212e+00 4.3944702e+00]\n", - " [1.3944356e+01 1.4526852e+01 1.4522321e+01 ... 1.1360933e+00\n", - " 1.1373357e+00 1.0925472e+00]\n", - " [2.1614985e-01 2.2517905e-01 2.2510880e-01 ... 1.7610380e-02\n", - " 1.7629640e-02 1.6935380e-02]]\n", - "\n", - " ...\n", - "\n", - " [[1.2135274e+01 1.2638837e+01 1.2631381e+01 ... 6.4981157e-01\n", - " 6.5124941e-01 6.2627065e-01]\n", - " [5.5483776e+01 5.7784695e+01 5.7749126e+01 ... 3.1669793e+00\n", - " 3.1733642e+00 3.0510781e+00]\n", - " [4.8090885e+01 5.0077457e+01 5.0038528e+01 ... 3.5894508e+00\n", - " 3.5939960e+00 3.4530318e+00]\n", - " ...\n", - " [2.0837215e+02 2.1699840e+02 2.1684923e+02 ... 1.7300163e+01\n", - " 1.7316896e+01 1.6632938e+01]\n", - " [5.4272240e+01 5.6520248e+01 5.6482670e+01 ... 4.5333900e+00\n", - " 4.5377030e+00 4.3584142e+00]\n", - " [4.2790710e+01 4.4562160e+01 4.4531502e+01 ... 3.6314108e+00\n", - " 3.6348450e+00 3.4912102e+00]]\n", - "\n", - " [[4.6256355e+01 4.8176720e+01 4.8149277e+01 ... 2.4131727e+00\n", - " 2.4187646e+00 2.3262236e+00]\n", - " [1.8563860e+02 1.9334552e+02 1.9323532e+02 ... 9.6885986e+00\n", - " 9.7110338e+00 9.3394794e+00]\n", - " [4.6820892e+01 4.8764538e+01 4.8736599e+01 ... 2.4593067e+00\n", - " 2.4649472e+00 2.3705857e+00]\n", - " ...\n", - " [7.3373207e+01 7.6411057e+01 7.6358841e+01 ... 6.0944810e+00\n", - " 6.1003828e+00 5.8594475e+00]\n", - " [9.6186623e+01 1.0017204e+02 1.0010677e+02 ... 7.9991369e+00\n", - " 8.0068121e+00 7.6905146e+00]\n", - " [1.7704167e+02 1.8437042e+02 1.8424315e+02 ... 1.4692220e+01\n", - " 1.4706187e+01 1.4125123e+01]]\n", - "\n", - " [[1.1533578e+01 1.2012404e+01 1.2005561e+01 ... 6.0168535e-01\n", - " 6.0307962e-01 5.8000606e-01]\n", - " [4.6254158e+01 4.8174438e+01 4.8146996e+01 ... 2.4129939e+00\n", - " 2.4185853e+00 2.3260510e+00]\n", - " [1.1533578e+01 1.2012404e+01 1.2005561e+01 ... 6.0168535e-01\n", - " 6.0307962e-01 5.8000606e-01]\n", - " ...\n", - " [8.4760414e+01 8.8270599e+01 8.8211227e+01 ... 7.0483670e+00\n", - " 7.0552197e+00 6.7765970e+00]\n", - " [1.8717400e+02 1.9493661e+02 1.9481715e+02 ... 1.5595144e+01\n", - " 1.5610263e+01 1.4993746e+01]\n", - " [8.5281387e+01 8.8815063e+01 8.8757294e+01 ... 7.0918350e+00\n", - " 7.0986452e+00 6.8182302e+00]]]\n" - ] - } - ], + "outputs": [], "source": [ "#print(rubixdata)" ] @@ -400,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -451,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/rubix/core/rotation.py b/rubix/core/rotation.py index 41ba090e..27879d8b 100644 --- a/rubix/core/rotation.py +++ b/rubix/core/rotation.py @@ -1,6 +1,6 @@ +import jax.numpy as jnp from beartype import beartype as typechecker from jaxtyping import jaxtyped -import jax.numpy as jnp from rubix.galaxy.alignment import rotate_galaxy as rotate_galaxy_core from rubix.logger import get_logger @@ -96,9 +96,9 @@ def rotate_galaxy(rubixdata: RubixData) -> RubixData: ), f"Velocities not found for {particle_type}. " assert masses is not None, f"Masses not found for {particle_type}. " - if config["galaxy"]["rotation"]=="matrix": - - rot_np = jnp.load('./data/rotation_matrix.npy') + if config["galaxy"]["rotation"] == "matrix": + + rot_np = jnp.load("./data/rotation_matrix.npy") rot_jax = jnp.array(rot_np) logger.info(f"Using rotation matrix from file: {rot_jax}.") rotation_matrix = rot_jax diff --git a/rubix/galaxy/alignment.py b/rubix/galaxy/alignment.py index 54854f8d..e7cfc2e0 100644 --- a/rubix/galaxy/alignment.py +++ b/rubix/galaxy/alignment.py @@ -238,7 +238,7 @@ def rotate_galaxy( alpha: float, beta: float, gamma: float, - R=None, # type: Float[Array, "3 3"] = None + R=None, # type: Float[Array, "3 3"] = None ) -> Tuple[Float[Array, "* 3"], Float[Array, "* 3"]]: """ Orientate the galaxy by applying a rotation matrix to the positions of the particles. diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index 89904c4c..658c09f3 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -76,8 +76,10 @@ def load_data(self): pynbody.analysis.angmom.faceon(halo.s) ang_mom_vec = pynbody.analysis.angmom.ang_mom_vec(halo.s) rotation_matrix = pynbody.analysis.angmom.calc_sideon_matrix(ang_mom_vec) - np.save('./data/rotation_matrix.npy', rotation_matrix) - self.logger.info("Rotation matrix calculated and saved to '/notebooks/data/rotation_matrix.npy'.") + np.save("./data/rotation_matrix.npy", rotation_matrix) + self.logger.info( + "Rotation matrix calculated and saved to '/notebooks/data/rotation_matrix.npy'." + ) self.sim = halo fields = self.pynbody_config["fields"] From 5344e9d8a7c758465237d3231bde1908522530e0 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 11 Jun 2025 16:18:47 +0200 Subject: [PATCH 48/76] fix failing pytests --- rubix/galaxy/input_handler/pynbody.py | 13 +++++++++---- tests/test_pynbody_handler.py | 17 +++++++++++++++++ 2 files changed, 26 insertions(+), 4 deletions(-) diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index 658c09f3..2e5bd9bc 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -76,10 +76,15 @@ def load_data(self): pynbody.analysis.angmom.faceon(halo.s) ang_mom_vec = pynbody.analysis.angmom.ang_mom_vec(halo.s) rotation_matrix = pynbody.analysis.angmom.calc_sideon_matrix(ang_mom_vec) - np.save("./data/rotation_matrix.npy", rotation_matrix) - self.logger.info( - "Rotation matrix calculated and saved to '/notebooks/data/rotation_matrix.npy'." - ) + if not os.path.exists("./data"): + self.logger.info( + "Rotation matrix calculated and not saved." + ) + else: + np.save("./data/rotation_matrix.npy", rotation_matrix) + self.logger.info( + "Rotation matrix calculated and saved to '/notebooks/data/rotation_matrix.npy'." + ) self.sim = halo fields = self.pynbody_config["fields"] diff --git a/tests/test_pynbody_handler.py b/tests/test_pynbody_handler.py index b8656811..f671f03b 100644 --- a/tests/test_pynbody_handler.py +++ b/tests/test_pynbody_handler.py @@ -97,6 +97,7 @@ def dm_getitem(key): @pytest.fixture def handler_with_mock_data(mock_simulation, mock_config): + """ with patch("pynbody.load", return_value=mock_simulation): with patch("pynbody.analysis.angmom.faceon", return_value=None): handler = PynbodyHandler( @@ -107,6 +108,22 @@ def handler_with_mock_data(mock_simulation, mock_config): halo_id=1, ) return handler + """ + with patch("pynbody.load", return_value=mock_simulation), \ + patch("pynbody.analysis.angmom.faceon", return_value=None), \ + patch("pynbody.analysis.angmom.ang_mom_vec", return_value=np.array([0.0,0.0,1.0])), \ + patch("pynbody.analysis.angmom.calc_sideon_matrix", return_value=np.eye(3)): + + handler = PynbodyHandler( + path="mock_path", + halo_path="mock_halo_path", + config=mock_config, + dist_z=mock_config["galaxy"]["dist_z"], + halo_id=1, + ) + return handler + + def test_pynbody_handler_initialization(handler_with_mock_data): From 35e1370ada9ede2c7c39117fefb162359b7cda4f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 11 Jun 2025 14:19:02 +0000 Subject: [PATCH 49/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rubix/galaxy/input_handler/pynbody.py | 4 +--- tests/test_pynbody_handler.py | 15 +++++++++------ 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index 2e5bd9bc..55a7ada6 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -77,9 +77,7 @@ def load_data(self): ang_mom_vec = pynbody.analysis.angmom.ang_mom_vec(halo.s) rotation_matrix = pynbody.analysis.angmom.calc_sideon_matrix(ang_mom_vec) if not os.path.exists("./data"): - self.logger.info( - "Rotation matrix calculated and not saved." - ) + self.logger.info("Rotation matrix calculated and not saved.") else: np.save("./data/rotation_matrix.npy", rotation_matrix) self.logger.info( diff --git a/tests/test_pynbody_handler.py b/tests/test_pynbody_handler.py index f671f03b..f2ac8301 100644 --- a/tests/test_pynbody_handler.py +++ b/tests/test_pynbody_handler.py @@ -109,10 +109,15 @@ def handler_with_mock_data(mock_simulation, mock_config): ) return handler """ - with patch("pynbody.load", return_value=mock_simulation), \ - patch("pynbody.analysis.angmom.faceon", return_value=None), \ - patch("pynbody.analysis.angmom.ang_mom_vec", return_value=np.array([0.0,0.0,1.0])), \ - patch("pynbody.analysis.angmom.calc_sideon_matrix", return_value=np.eye(3)): + with ( + patch("pynbody.load", return_value=mock_simulation), + patch("pynbody.analysis.angmom.faceon", return_value=None), + patch( + "pynbody.analysis.angmom.ang_mom_vec", + return_value=np.array([0.0, 0.0, 1.0]), + ), + patch("pynbody.analysis.angmom.calc_sideon_matrix", return_value=np.eye(3)), + ): handler = PynbodyHandler( path="mock_path", @@ -123,8 +128,6 @@ def handler_with_mock_data(mock_simulation, mock_config): ) return handler - - def test_pynbody_handler_initialization(handler_with_mock_data): """Test initialization of PynbodyHandler.""" From a1743b596302e18b4bc51f84d013a6fe68f264f8 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 11 Jun 2025 16:22:32 +0200 Subject: [PATCH 50/76] delete notebook --- ...e_single_function_shard_map_fits_gsf.ipynb | 361 ------------------ 1 file changed, 361 deletions(-) delete mode 100644 notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb deleted file mode 100644 index abfe7e4d..00000000 --- a/notebooks/rubix_pipeline_single_function_shard_map_fits_gsf.ipynb +++ /dev/null @@ -1,361 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from jax import config\n", - "#config.update(\"jax_enable_x64\", True)\n", - "config.update('jax_num_cpu_devices', 2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import os\n", - "\n", - "# Tell XLA to fake 2 host CPU devices\n", - "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", - "\n", - "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", - "\n", - "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", - "\n", - "import jax\n", - "\n", - "# Now JAX will list two CpuDevice entries\n", - "print(jax.devices())\n", - "# → [CpuDevice(id=0), CpuDevice(id=1)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "#import os\n", - "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", - "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Config" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "from rubix.core.pipeline import RubixPipeline \n", - "import os\n", - "\n", - "galaxy_id = \"g7.66e11\"\n", - "\n", - "config_NIHAO = {\n", - " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"NihaoHandler\",\n", - " \"args\": {\n", - " \"particle_type\": [\"stars\"],\n", - " \"save_data_path\": \"data\",\n", - " \"snapshot\": \"1024\",\n", - " },\n", - " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": False, \"subset_size\": 200000},\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"NIHAO\",\n", - " \"args\": {\n", - " \"path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024',\n", - " \"halo_path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024.z0.000.AHF_halos',\n", - " \"halo_id\": 0,\n", - " },\n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE_WFM\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.01,\n", - " \"rotation\": {\"type\": \"matrix\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"BruzualCharlot2003\" #\"Mastar_CB19_SLOG_1_5\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Pipeline yaml" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Run the pipeline\n", - "\n", - "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config_NIHAO)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "\n", - "inputdata = pipe.prepare_data()\n", - "rubixdata = pipe.run_sharded(inputdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#print(rubixdata)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert luminosity to flux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from rubix.spectra.ifu import convert_luminoisty_to_flux\n", - "from rubix.cosmology import PLANCK15\n", - "\n", - "observation_lum_dist = PLANCK15.luminosity_distance_to_z(config_NIHAO[\"galaxy\"][\"dist_z\"])\n", - "observation_z = config_NIHAO[\"galaxy\"][\"dist_z\"]\n", - "pixel_size = 1.0\n", - "fluxcube = convert_luminoisty_to_flux(rubixdata, observation_lum_dist, observation_z, pixel_size)\n", - "rubixdata = fluxcube/1e-20" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Store datacube in a fits file with header" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "#from rubix.core.fits import store_fits\n", - "\n", - "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_ultraWFM\":\n", - "# cutted_datatcube = data.stars.datacube[300:600, :, :]\n", - "# data.stars.datacube = cutted_datatcube\n", - "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_WFM\":\n", - "# cutted_datatcube = data.stars.datacube[100:200, :, :]\n", - "# data.stars.datacube = cutted_datatcube\n", - "\n", - "#store_fits(config_NIHAO, rubixdata, \"./output/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Mock-data\n", - "\n", - "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import jax.numpy as jnp\n", - "\n", - "wave = pipe.telescope.wave_seq\n", - "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", - "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "wave = pipe.telescope.wave_seq\n", - "\n", - "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", - "spectra_sharded = rubixdata # Spectra of all stars\n", - "#print(spectra.shape)\n", - "\n", - "plt.figure(figsize=(10, 5))\n", - "#plt.subplot(1, 2, 1)\n", - "#plt.title(\"Rubix\")\n", - "#plt.xlabel(\"Wavelength [Angstrom]\")\n", - "#plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "#plt.plot(wave, spectra[12,12,:])\n", - "#plt.plot(wave, spectra[8,12,:])\n", - "\n", - "#plt.subplot(1, 2, 2)\n", - "plt.title(\"Rubix Sharded\")\n", - "plt.xlabel(\"Wavelength [Angstrom]\")\n", - "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "plt.plot(wave, spectra_sharded[21,15,:])\n", - "plt.plot(wave, spectra_sharded[15,21,:])\n", - "plt.plot(wave, spectra_sharded[13,4,:])\n", - "plt.plot(wave, spectra_sharded[4,13,:])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot a spacial image of the data cube" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import numpy as np\n", - "# get the spectra of the visible wavelengths from the ifu cube\n", - "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", - "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", - "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", - "#visible_spectra.shape\n", - "\n", - "#image = jnp.sum(visible_spectra, axis=2)\n", - "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", - "img32 = np.array(sharded_image, dtype=np.float32)\n", - "\n", - "# Plot side by side\n", - "plt.figure(figsize=(6, 5))\n", - "\n", - "# Original IFU datacube image\n", - "#im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", - "#axes[0].set_title(\"Original IFU Datacube\")\n", - "#fig.colorbar(im0, ax=axes[0])\n", - "\n", - "# Sharded IFU datacube image\n", - "plt.imshow(img32, origin=\"lower\", cmap=\"inferno\", vmin=0, vmax=1e5)\n", - "plt.title(\"Sharded IFU Datacube\")\n", - "plt.colorbar(label=\"Flux [erg/s/cm^2]\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DONE!\n", - "\n", - "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 30fb69e1ac489fc3d95b60164a46c54a2c55691c Mon Sep 17 00:00:00 2001 From: anschaible <131476730+anschaible@users.noreply.github.com> Date: Tue, 17 Jun 2025 13:52:50 +0200 Subject: [PATCH 51/76] Update rubix/core/ifu.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- rubix/core/ifu.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 2db4a343..86b60e91 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -30,8 +30,8 @@ @jaxtyped(typechecker=typechecker) def get_calculate_spectra(config: dict) -> Callable: """ - This function is outdates, we do not recomend to use it for a large set of particles! - We recommend to use the function get_calculate_datacube_particlewise! + This function is outdated, we do not recommend using it for a large set of particles! + We recommend using the function get_calculate_datacube_particlewise! The function gets the lookup function that performs the lookup to the SSP model, and parallelizes the funciton across all GPUs. From 1422247a26a2cbe56c08bd41e262103441363137 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 2 Jul 2025 11:57:20 +0200 Subject: [PATCH 52/76] scaling relation on cpu --- ...bix_pipeline_single_function_scaling.ipynb | 416 ++++++++++++++++++ 1 file changed, 416 insertions(+) create mode 100644 notebooks/rubix_pipeline_single_function_scaling.ipynb diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb new file mode 100644 index 00000000..743bcb18 --- /dev/null +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -0,0 +1,416 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "from jax import config\n", + "#config.update(\"jax_enable_x64\", True)\n", + "config.update('jax_num_cpu_devices', 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0), CpuDevice(id=1)]\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import os\n", + "\n", + "# Tell XLA to fake 2 host CPU devices\n", + "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", + "\n", + "# Only make GPU 0 and GPU 1 visible to JAX:\n", + "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", + "\n", + "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", + "\n", + "import jax\n", + "\n", + "# Now JAX will list two CpuDevice entries\n", + "print(jax.devices())\n", + "# → [CpuDevice(id=0), CpuDevice(id=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# NBVAL_SKIP\n", + "#import os\n", + "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", + "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", + "#os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RUBIX pipeline\n", + "\n", + "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", + "\n", + "## How to use the Pipeline\n", + "1) Define a `config`\n", + "2) Setup the `pipeline yaml`\n", + "3) Run the RUBIX pipeline\n", + "4) Do science with the mock-data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Config\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 10:19:40,907 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-07-02 10:19:40,908 - rubix - INFO - Rubix version: 0.0.post427+g131f0ec.d20250602\n", + "2025-07-02 10:19:40,908 - rubix - INFO - JAX version: 0.5.0\n", + "2025-07-02 10:19:40,908 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import matplotlib.pyplot as plt\n", + "from rubix.core.pipeline import RubixPipeline \n", + "import os\n", + "\n", + "config_TNG = {\n", + " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", + " \n", + " \"logger\": {\n", + " \"log_level\": \"DEBUG\",\n", + " \"log_file_path\": None,\n", + " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", + " },\n", + " \"data\": {\n", + " \"name\": \"IllustrisAPI\",\n", + " \"args\": {\n", + " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", + " \"particle_type\": [\"stars\"],\n", + " \"simulation\": \"TNG50-1\",\n", + " \"snapshot\": 99,\n", + " \"save_data_path\": \"data\",\n", + " },\n", + " \n", + " \"load_galaxy_args\": {\n", + " \"id\": 11,\n", + " \"reuse\": True,\n", + " },\n", + " \n", + " \"subset\": {\n", + " \"use_subset\": True,\n", + " \"subset_size\": 500000,\n", + " },\n", + " },\n", + " \"simulation\": {\n", + " \"name\": \"IllustrisTNG\",\n", + " \"args\": {\n", + " \"path\": \"data/galaxy-id-11.hdf5\",\n", + " },\n", + " \n", + " },\n", + " \"output_path\": \"output\",\n", + "\n", + " \"telescope\":\n", + " {\"name\": \"MUSE\",\n", + " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", + " \"lsf\": {\"sigma\": 0.5},\n", + " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", + " \"cosmology\":\n", + " {\"name\": \"PLANCK15\"},\n", + " \n", + " \"galaxy\":\n", + " {\"dist_z\": 0.1,\n", + " \"rotation\": {\"type\": \"edge-on\"},\n", + " },\n", + " \n", + " \"ssp\": {\n", + " \"template\": {\n", + " \"name\": \"FSPS\", #\"Mastar_CB19_SLOG_1_5\"\n", + " },\n", + " \"dust\": {\n", + " \"extinction_model\": \"Cardelli89\",\n", + " \"dust_to_gas_ratio\": 0.01,\n", + " \"dust_to_metals_ratio\": 0.4,\n", + " \"dust_grain_density\": 3.5,\n", + " \"Rv\": 3.1,\n", + " },\n", + " }, \n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Run the pipeline\n", + "\n", + "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "pipe = RubixPipeline(config_TNG)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 10:19:41,320 - rubix - INFO - Getting rubix data...\n", + "2025-07-02 10:19:41,321 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-07-02 10:19:41,400 - rubix - INFO - Centering stars particles\n", + "2025-07-02 10:19:41,939 - rubix - WARNING - The Subset value is set in config. Using only subset of size 500000 for stars\n", + "2025-07-02 10:19:41,941 - rubix - INFO - Data loaded with 500000 star particles and 0 gas particles.\n" + ] + }, + { + "data": { + "text/plain": [ + "(200000000, 3)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#NBVAL_SKIP\n", + "import jax.numpy as jnp\n", + "\n", + "inputdata = pipe.prepare_data()\n", + "coords = inputdata.stars.coords\n", + "vel = inputdata.stars.velocity\n", + "mass = inputdata.stars.mass\n", + "age = inputdata.stars.age\n", + "met = inputdata.stars.metallicity\n", + "factor = 10\n", + "inputdata.stars.coords = jnp.concatenate([coords]*factor, axis=0)\n", + "inputdata.stars.velocity = jnp.concatenate([vel]*factor, axis=0)\n", + "inputdata.stars.mass = jnp.concatenate([mass]*factor, axis=0)\n", + "inputdata.stars.age = jnp.concatenate([age]*factor, axis=0)\n", + "inputdata.stars.metallicity = jnp.concatenate([met]*factor, axis=0)\n", + "inputdata.stars.coords.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 10:20:30,105 - rubix - INFO - Setting up the pipeline...\n", + "2025-07-02 10:20:30,108 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-07-02 10:20:30,130 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-07-02 10:20:30,149 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 10:20:30,203 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 10:20:30,469 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 10:20:30,476 - rubix - INFO - Getting cosmology...\n", + "2025-07-02 10:20:30,605 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 10:20:30,647 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 10:20:30,661 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 10:20:30,704 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 10:20:30,903 - rubix - INFO - Assembling the pipeline...\n", + "2025-07-02 10:20:30,903 - rubix - INFO - Compiling the expressions...\n", + "2025-07-02 10:20:30,904 - rubix - INFO - Number of devices: 2\n", + "2025-07-02 10:21:15,519 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-07-02 10:21:19,837 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-07-02 10:21:20,205 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-07-02 10:21:20,389 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-07-02 10:21:30,549 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-07-02 10:21:30,593 - rubix - INFO - Convolving with PSF...\n", + "2025-07-02 10:21:30,612 - rubix - INFO - Convolving with LSF...\n", + "2025-07-02 10:21:30,616 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-07-02 10:21:57,084 - rubix - INFO - Pipeline run completed in 86.98 seconds.\n" + ] + } + ], + "source": [ + "#NBVAL_SKIP\n", + "\n", + "rubixdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "particle_number = jnp.array([1, 10, 100, 1e3, 1e4, 1e5, 5e5, 5e5*2, 5e5*20, 5e5*50, 5e5*100, 5e5*150, 5e5*200, 5e5*300, 5e5*400])\n", + "time_on_mac = jnp.array([2.14, 2.14, 2.24, 2.2, 2.2 ,2.15, 2.34, 2.26, 2.50, 3.78, 16.88, 38.92, 56.29, 72.27, 86.98]) #seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Scaling of Rubix Pipeline with Number of Particles')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(particle_number, time_on_mac, marker='o', linestyle='-', color='b')\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.xlabel('Number of Particles')\n", + "plt.ylabel('Time (seconds)')\n", + "plt.title('Scaling of Rubix Pipeline with Number of Particles')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Scaling of Rubix Pipeline with Number of Particles')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(particle_number, time_on_mac/particle_number, marker='o', linestyle='-', color='b')\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.xlabel('Number of Particles')\n", + "plt.ylabel('Time (seconds) per Particle')\n", + "plt.title('Scaling of Rubix Pipeline with Number of Particles')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rubix", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 07cb666ef389cf1a1ef404d36e9e4617aa2e1a59 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 2 Jul 2025 09:58:17 +0000 Subject: [PATCH 53/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- ...bix_pipeline_single_function_scaling.ipynb | 176 ++---------------- 1 file changed, 16 insertions(+), 160 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index 743bcb18..91f06eec 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -14,17 +14,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0), CpuDevice(id=1)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -46,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -84,26 +76,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-02 10:19:40,907 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-07-02 10:19:40,908 - rubix - INFO - Rubix version: 0.0.post427+g131f0ec.d20250602\n", - "2025-07-02 10:19:40,908 - rubix - INFO - JAX version: 0.5.0\n", - "2025-07-02 10:19:40,908 - rubix - INFO - Running on [CpuDevice(id=0), CpuDevice(id=1)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -186,18 +161,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -207,29 +173,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-02 10:19:41,320 - rubix - INFO - Getting rubix data...\n", - "2025-07-02 10:19:41,321 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-07-02 10:19:41,400 - rubix - INFO - Centering stars particles\n", - "2025-07-02 10:19:41,939 - rubix - WARNING - The Subset value is set in config. Using only subset of size 500000 for stars\n", - "2025-07-02 10:19:41,941 - rubix - INFO - Data loaded with 500000 star particles and 0 gas particles.\n" - ] - }, - { - "data": { - "text/plain": [ - "(200000000, 3)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import jax.numpy as jnp\n", @@ -251,55 +195,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-02 10:20:30,105 - rubix - INFO - Setting up the pipeline...\n", - "2025-07-02 10:20:30,108 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-07-02 10:20:30,130 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-07-02 10:20:30,149 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 10:20:30,203 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 10:20:30,469 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 10:20:30,476 - rubix - INFO - Getting cosmology...\n", - "2025-07-02 10:20:30,605 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 10:20:30,647 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 10:20:30,661 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 10:20:30,704 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 10:20:30,903 - rubix - INFO - Assembling the pipeline...\n", - "2025-07-02 10:20:30,903 - rubix - INFO - Compiling the expressions...\n", - "2025-07-02 10:20:30,904 - rubix - INFO - Number of devices: 2\n", - "2025-07-02 10:21:15,519 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-07-02 10:21:19,837 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-07-02 10:21:20,205 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-07-02 10:21:20,389 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-07-02 10:21:30,549 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-07-02 10:21:30,593 - rubix - INFO - Convolving with PSF...\n", - "2025-07-02 10:21:30,612 - rubix - INFO - Convolving with LSF...\n", - "2025-07-02 10:21:30,616 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-07-02 10:21:57,084 - rubix - INFO - Pipeline run completed in 86.98 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -308,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -319,30 +217,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Scaling of Rubix Pipeline with Number of Particles')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10, 6))\n", @@ -356,30 +233,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Scaling of Rubix Pipeline with Number of Particles')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10, 6))\n", From 5da0011e46e21dfa26a22e6a3af2778ceace1050 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 2 Jul 2025 15:45:53 +0200 Subject: [PATCH 54/76] Timing on gpus --- ...bix_pipeline_single_function_scaling.ipynb | 208 ++++++++++++++++-- 1 file changed, 184 insertions(+), 24 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index 91f06eec..af2796a7 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,9 +14,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)]\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -25,7 +33,7 @@ "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '1,3,5,6,7,8'\n", "\n", "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", @@ -38,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -46,8 +54,8 @@ "#import os\n", "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", + "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" ] }, @@ -76,9 +84,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 15:36:42,036 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-07-02 15:36:42,036 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", + "2025-07-02 15:36:42,037 - rubix - INFO - JAX version: 0.6.0\n", + "2025-07-02 15:36:42,037 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -161,9 +186,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -171,9 +205,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 15:36:42,879 - rubix - INFO - Getting rubix data...\n", + "2025-07-02 15:36:42,880 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-07-02 15:36:42,966 - rubix - INFO - Centering stars particles\n", + "2025-07-02 15:36:45,038 - rubix - WARNING - The Subset value is set in config. Using only subset of size 500000 for stars\n", + "2025-07-02 15:36:45,039 - rubix - INFO - Data loaded with 500000 star particles and 0 gas particles.\n" + ] + }, + { + "data": { + "text/plain": [ + "(10000000, 3)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#NBVAL_SKIP\n", "import jax.numpy as jnp\n", @@ -184,7 +240,7 @@ "mass = inputdata.stars.mass\n", "age = inputdata.stars.age\n", "met = inputdata.stars.metallicity\n", - "factor = 10\n", + "factor = 20\n", "inputdata.stars.coords = jnp.concatenate([coords]*factor, axis=0)\n", "inputdata.stars.velocity = jnp.concatenate([vel]*factor, axis=0)\n", "inputdata.stars.mass = jnp.concatenate([mass]*factor, axis=0)\n", @@ -195,9 +251,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 15:36:45,348 - rubix - INFO - Setting up the pipeline...\n", + "2025-07-02 15:36:45,350 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-07-02 15:36:45,351 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-07-02 15:36:45,353 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:45,372 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:45,782 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:45,800 - rubix - INFO - Getting cosmology...\n", + "2025-07-02 15:36:45,879 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:46,165 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:46,183 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:46,341 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:46,830 - rubix - INFO - Assembling the pipeline...\n", + "2025-07-02 15:36:46,830 - rubix - INFO - Compiling the expressions...\n", + "2025-07-02 15:36:46,831 - rubix - INFO - Number of devices: 6\n", + "2025-07-02 15:36:47,812 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-07-02 15:36:47,920 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-07-02 15:36:47,926 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-07-02 15:36:47,954 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-07-02 15:36:48,157 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-07-02 15:36:48,158 - rubix - INFO - Convolving with PSF...\n", + "2025-07-02 15:36:48,163 - rubix - INFO - Convolving with LSF...\n", + "2025-07-02 15:36:48,173 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-07-02 15:45:06,127 - rubix - INFO - Pipeline run completed in 500.78 seconds.\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "\n", @@ -206,13 +308,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import jax.numpy as jnp\n", - "particle_number = jnp.array([1, 10, 100, 1e3, 1e4, 1e5, 5e5, 5e5*2, 5e5*20, 5e5*50, 5e5*100, 5e5*150, 5e5*200, 5e5*300, 5e5*400])\n", - "time_on_mac = jnp.array([2.14, 2.14, 2.24, 2.2, 2.2 ,2.15, 2.34, 2.26, 2.50, 3.78, 16.88, 38.92, 56.29, 72.27, 86.98]) #seconds" + "gpu_number = jnp.array([1, 2, 3, 4, 5, 6, 7])\n", + "time_on_compgpu4_5e5mal2 = jnp.array([274.27, 152.38, 108.70, 88.38, 88.97, 71.85, 62.91])\n", + "time_on_compgpu4_5e5mal1 = jnp.array([151.12, 77.44, 63.81, 50.88, 48.34, 48.1, 41.60])" ] }, { @@ -220,10 +323,46 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "particle_number = jnp.array([1, 10, 100, 1e3, 1e4, 1e5, 5e5, 5e5*2, 5e5*20, 5e5*50, 5e5*100, 5e5*150, 5e5*200, 5e5*300, 5e5*400])\n", + "time_on_mac_2cpu = jnp.array([2.14, 2.14, 2.24, 2.2, 2.2 ,2.15, 2.34, 2.26, 2.50, 3.78, 16.88, 38.92, 56.29, 72.27, 86.98]) #seconds\n", + "particle_number_gpu = jnp.array([1, 10, 100, 1e3, 1e4, 1e5, 5e5, 5e5*2, 5e5*4, 5e5*20])\n", + "time_on_compgpu4_2gpu = jnp.array([18.01, 18.64, 18.44, 18.58, 20.43, 31.14, 84.95, 138.29, 255.22, 1182.35])\n", + "time_on_compgpu4_4gpu = jnp.array([19.11, 19.18, 19.69, 19.26, 20.74, 27.97, 59.56, 89.58, 142.98, 707.86])\n", + "time_on_compgpu4_6gpu = jnp.array([20.14, 20.22, 20.34, 20.85, 20.48, 25.59, 47.19, 76.89, 122.12, 500.78])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Scaling of Rubix Pipeline with Number of Particles')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10, 6))\n", - "plt.plot(particle_number, time_on_mac, marker='o', linestyle='-', color='b')\n", + "plt.plot(particle_number, time_on_mac_2cpu, marker='o', linestyle='-')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel('Number of Particles')\n", @@ -233,13 +372,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Scaling of Rubix Pipeline with Number of Particles')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10, 6))\n", - "plt.plot(particle_number, time_on_mac/particle_number, marker='o', linestyle='-', color='b')\n", + "plt.plot(particle_number, time_on_mac_2cpu/particle_number, marker='o', linestyle='-')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.xlabel('Number of Particles')\n", @@ -264,7 +424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.11.9" } }, "nbformat": 4, From 8867c6b26885b99bdfb1c9f29601ae34b18c7198 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 2 Jul 2025 13:48:50 +0000 Subject: [PATCH 55/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- ...bix_pipeline_single_function_scaling.ipynb | 178 ++---------------- 1 file changed, 17 insertions(+), 161 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index af2796a7..2eb26708 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -14,17 +14,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -46,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -84,26 +76,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-02 15:36:42,036 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-07-02 15:36:42,036 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", - "2025-07-02 15:36:42,037 - rubix - INFO - JAX version: 0.6.0\n", - "2025-07-02 15:36:42,037 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -186,18 +161,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -205,31 +171,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-02 15:36:42,879 - rubix - INFO - Getting rubix data...\n", - "2025-07-02 15:36:42,880 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-07-02 15:36:42,966 - rubix - INFO - Centering stars particles\n", - "2025-07-02 15:36:45,038 - rubix - WARNING - The Subset value is set in config. Using only subset of size 500000 for stars\n", - "2025-07-02 15:36:45,039 - rubix - INFO - Data loaded with 500000 star particles and 0 gas particles.\n" - ] - }, - { - "data": { - "text/plain": [ - "(10000000, 3)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import jax.numpy as jnp\n", @@ -251,55 +195,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-02 15:36:45,348 - rubix - INFO - Setting up the pipeline...\n", - "2025-07-02 15:36:45,350 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-07-02 15:36:45,351 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-07-02 15:36:45,353 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 15:36:45,372 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 15:36:45,782 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 15:36:45,800 - rubix - INFO - Getting cosmology...\n", - "2025-07-02 15:36:45,879 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 15:36:46,165 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 15:36:46,183 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 15:36:46,341 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-02 15:36:46,830 - rubix - INFO - Assembling the pipeline...\n", - "2025-07-02 15:36:46,830 - rubix - INFO - Compiling the expressions...\n", - "2025-07-02 15:36:46,831 - rubix - INFO - Number of devices: 6\n", - "2025-07-02 15:36:47,812 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-07-02 15:36:47,920 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-07-02 15:36:47,926 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-07-02 15:36:47,954 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-07-02 15:36:48,157 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-07-02 15:36:48,158 - rubix - INFO - Convolving with PSF...\n", - "2025-07-02 15:36:48,163 - rubix - INFO - Convolving with LSF...\n", - "2025-07-02 15:36:48,173 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-07-02 15:45:06,127 - rubix - INFO - Pipeline run completed in 500.78 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -308,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -335,30 +233,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Scaling of Rubix Pipeline with Number of Particles')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10, 6))\n", @@ -372,30 +249,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Scaling of Rubix Pipeline with Number of Particles')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10, 6))\n", From f94b12c0445770a5af39d932ad2c03728f946518 Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 2 Jul 2025 16:05:21 +0200 Subject: [PATCH 56/76] scaling relations for computation --- ...bix_pipeline_single_function_scaling.ipynb | 350 ++++++++++++++++-- 1 file changed, 324 insertions(+), 26 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index 2eb26708..c4bf8893 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,9 +14,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)]\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -38,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -76,9 +84,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 15:36:42,036 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-07-02 15:36:42,036 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", + "2025-07-02 15:36:42,037 - rubix - INFO - JAX version: 0.6.0\n", + "2025-07-02 15:36:42,037 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -161,9 +186,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -171,9 +205,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 15:36:42,879 - rubix - INFO - Getting rubix data...\n", + "2025-07-02 15:36:42,880 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-07-02 15:36:42,966 - rubix - INFO - Centering stars particles\n", + "2025-07-02 15:36:45,038 - rubix - WARNING - The Subset value is set in config. Using only subset of size 500000 for stars\n", + "2025-07-02 15:36:45,039 - rubix - INFO - Data loaded with 500000 star particles and 0 gas particles.\n" + ] + }, + { + "data": { + "text/plain": [ + "(10000000, 3)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#NBVAL_SKIP\n", "import jax.numpy as jnp\n", @@ -195,9 +251,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-02 15:36:45,348 - rubix - INFO - Setting up the pipeline...\n", + "2025-07-02 15:36:45,350 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-07-02 15:36:45,351 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-07-02 15:36:45,353 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:45,372 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:45,782 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:45,800 - rubix - INFO - Getting cosmology...\n", + "2025-07-02 15:36:45,879 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:46,165 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:46,183 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:46,341 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-02 15:36:46,830 - rubix - INFO - Assembling the pipeline...\n", + "2025-07-02 15:36:46,830 - rubix - INFO - Compiling the expressions...\n", + "2025-07-02 15:36:46,831 - rubix - INFO - Number of devices: 6\n", + "2025-07-02 15:36:47,812 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-07-02 15:36:47,920 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-07-02 15:36:47,926 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-07-02 15:36:47,954 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-07-02 15:36:48,157 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-07-02 15:36:48,158 - rubix - INFO - Convolving with PSF...\n", + "2025-07-02 15:36:48,163 - rubix - INFO - Convolving with LSF...\n", + "2025-07-02 15:36:48,173 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-07-02 15:45:06,127 - rubix - INFO - Pipeline run completed in 500.78 seconds.\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "\n", @@ -206,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -218,7 +320,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(5,3))\n", + "plt.plot(gpu_number, time_on_compgpu4_5e5mal2, marker='o', label='1e6 particles')\n", + "plt.plot(gpu_number, time_on_compgpu4_5e5mal1, marker='o', label='5e5 particles')\n", + "plt.xlabel('Number of GPUs')\n", + "plt.ylabel('Time in seconds on RTX 2080ti')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -231,36 +369,196 @@ "time_on_compgpu4_6gpu = jnp.array([20.14, 20.22, 20.34, 20.85, 20.48, 25.59, 47.19, 76.89, 122.12, 500.78])" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VBJvcuSR85kDXSMeQx4LKkNE0IJrWxDCMffL7/ptQqTXoULcymlQr4N4c1F7rrSPjw+T9VabdTvuVp2impqaKAs8U/EM3UKpConPrkRVCFga5AskCsVlIMD8zLSrmQ373u8Dc6tJ2/+gu4FRB0q70G1H9SqnzUCkhflJSkqiBaQi5aMk9S/Tr108Ee+WFrHVqp7qbY8aMgTmQZTtgwAA0bdpUrNeubRlpshiGKXuS07Lx57HoohOzk5FBw1urTdVuznm47zNXmjFSmoFAVDyYUuZRJROafkLVTehFy1u2bBHbaB+mfKFkE/Q7LF++/LFLaK1fv14E7dB4dnp6utE2Cvwhq5AsTBrfnDhxIsaNG2fW+cePHy+s4A4dOmD69OlCeBmGsU+WHYlCWpYKDat4oHM9n8J3pmGtmp3zt5OFWYrDXmZZmuvWrROCSSnzDKH5mjTmRdNRqHQYjaHZLOQiJYtPChS5tXxg0fsNWSvNjUCfLQEK1IqLi0NISIiRNUgu0B9++AGZmZlGuYMJX19fVKxYUW9V6qB6pgTNxyVL1OiyhwzBxx9/LEQzICAAcrncaM4u1aTLi+4cOpf4a6+9JgSZ/q4occacOXNECbq3335bUl8ZhrENMpUqLD54Uz+WWdRwEB7FA7cOa5f7fgm4emvHMOleWkoWptmWJo17OTnlmS9jAG2jfWwa+iHJRSrlVae79qlH5y7IfzLAs5p2PynnkzieSYXCz5w5IyxE3YtyBpPI0XJewSRI8AYNGoRly5bh7l1pDwUkfHXr1hUZoQwFkyA37+3bt4UnwhByGZP1qxNjonr16qKsHE1folSNNv3QxTCMSTacvIP4h5kI8HJBv+YShsCO/w6osoBqrYDQ14CmA4FanUpdMM0STbIiaczq5MmT+bZRG7nmaNyLyeN3F8jKzO9OVmGTJk2MXhRkQ3MuabkgaMoQCWBYWJjwGpCr9Nq1a8JFe+jQIZNiWxBkPZJw0jj3wYMHRYHytWvX4n//+59wHevORXNH//33X9y4cUMI6q5du9CoUaMS+R4YhrEO1GqNPpnBqA614KgoQpaUmcCx37TLbd8wK0CyTN2z5Np7+eWXRXAIzcfz8/MT7eQKJLcb3ShpH8YA8quTf93kPM25pT5P0xxIVGlcmuql0rQPEjKyICkR/+DBgyUlR9Dh4OAg3K00jYWEkwKNatWqJQRz0qRJRm5jiqAlq5Rcun369MHXX39dSj1kGMYS2XkxDtfiU+Hh7IAXwyQERp5dB6TGAR5VgeBnUdbINHkn4BUBJTg4fPiw0ZQTygjUsGFDWOM8TZqXSONvdNM2hAKcSDjoZv+4ATVi+gmNcdJk2zLyu9saJfp7MAxjMQz6+RCO3kzE2C618eGTRXiaSK5+6QTEngF6TAc65T6EPy66KYim9OCx5mmS+8zaXWjFnqdZXEggyd/OMAzD6DkZ/UAIpqNCJlyzRRJ1QCuYDq5Aq1dQHkge09RBrjTKNpOX7OxsEaHJMAzDMFLQjWU+26Ia/D0leJAOL9C+N38RcNPmCrBY0YyJiRFBIpRWjaYnDB8+3Eg8KckB555lGIZhpHAzIRXh52ILT5lnSOIN4OI/2uW25s0JLxfRnDJliggMoRR64eHhIuUZiaRh9Qszh0cZhmEYO07MrtEA3Rr4or5/bm3mAjn6qzaTWt2egK82e5lKrcKx2GPYcn2LeKf10kbymOb27dvF9AOa80ccOHAAL7zwArp3744dO3aItiInpDIMwzB2T8KjTKw9cbvolHk6MlKAiKVGVub2qO2Ye3Qu7qXlzgf3d/PHlLAp6BnUs/wtTYoooqkmOqiuJk1Ip8oUZHHS1BOGYRiGKYolh6KQqVSjWaAX2taWMDZ5chmQ9RDwaQDU6SEEc9LuSUaCScSlxYl22l7uoknJtPPmBqX5eGvWrBHbKPkBwzAMwxRGepYKSw+ZkTKPXK5HftYut30dKo1aWJgaExVOdG3zjs4rNVetZNF88skn8euv5FOGSeFs0aJFSV8bwzAMY2OsOXELD9KyUb2SK/o0llBS8tJWICkKcKkINHsREXER+SzMvMIZmxYr9ivXMc3Zs2cjLc10LUkSTkrofufOHVgDZVaEmmEYhtFDtTJ/23dDLL/WsTYcikqZZzjNpPVIwMkN8Wnx0koeStyv1CxNEsZCsyQ4OIjpKNYAJTag6F+p9SYZhmGYxyf8bCyiE9NQ0c0RL7QOLPqAmEggaj8gdwBCR4smXzdfSZ8ldb9ST27AmE95hEWT1T906FCRU5bKd1Gh5+PHjxd6TFZWlsg7S2XFKMk7ZUtq3ry5SLRuWP3klVdeEeMQ9KLqNlTt5NNPP4VSqRTbFy9eLObymoKO2bBhQwn3lmEYS0ejocTs18Ty8LZBcHNykG5lBvcHvKqJxRC/EBElWxAyyFDFrYrYrzQwO40eYx7lERZNc2epsDNFNW/dulXUy7xy5YpR9HNeqM4m1UWlYK8ZM2aI4+k4yve6cuVKfP/996LepQ5Krr5o0SJxHBUhJ+vd0dERH374Yan0iWEY6+bIjURE3k6Gs4Mcw9vXLPqAh/eAM2tzq5nkoJArxP1z4u6JJgWTmBw2WexXGrBoliK6sOi8UV66sOj5XeeXinBSpRKqU0mipoMSnRcGVRfZv3+/sEZbtmypb6fal126dMmXuIKmHFGyfoLKwtEc3k2bNpklmiTub731lqiIQtmlAgMDRWWUkSNHmtFbhmGsKWXegFaB8HF3LvqA4/8HqLOBwDAgsJXRpoAKASYPIYOEBLM052myaJoBCUe6Ml3SvuSCnXN0TqFh0WSBtqnSRtITkauDq+TkESReVKqNkk/s2bNH1Ml84403MHq0dkzAFGRNPvHEE0aCaUhRn00u4Pv378Mcpk6dKsaWyRr28fHB1atXkZ4u7ftlGMZ6uHLvoSgBRreR0Z0kpMzLzgCO/V+BKfN+jtROQXmq1lMYUH+ACPqhMUxyyZaWhWm2aNINbvr06SLgxxTR0dF49dVX8d9//8FWIcFss6JNiZ2PXLbt/2wvad8jLx+Bm6ObpH2p6POCBQtE7Uqy3Cjgafz48WL8ccSIESaPoRJpXbt2NWp77rnn9L9ns2bNREFpUw8SlBGKikm//fbbMAf6myGR1mWZokQZDMPYrpXZK9gftXwqFH3A2bVAWgLgGQg0Mq47fP7+eey+vRtymRxjm49FLS8J1VHKIxDojz/+QGhoKM6ePZtv2y+//IImTZoUKKhM2aJWq0Uwz2effSZEacyYMcLK/PnnnAnCEvnpp59w6tQpjBo1Kt90o82bN8Pd3V3UtqQ5vFSo+pNPPjHr/OTW/fPPP8Uc3w8++MCkKDMMY93cS8nAhlN3pKfMo6EgXQBQmzGAwlhXFkRqtz1Z68kyF0xCssqRWNL4E1kFZHFOnjxZlAmjGypZMl9++aW4Odsy5CIli08KJ+6dwBs7cgevC+KnHj+hlX8rSZ8tlYCAAAQHBxu1UQ1UmktbEPXq1cOlS5fynYeoVCl/misKMiJrlqzXqlWrGj0w0dSk1NRUId6U5F9HUlKSeNfVMCWxjYqKEoFEZNH26NFDBBTR3xLDMLbBogM3ka3SoHWQN1oFFRyMqOfGXuDeWYA8ayHDjTZduH8Bu2/tFgE/Y5qVj95ItjTpRrhkyRKsWrUK3377rbBkaBoDjXVRxKWtCyZBfSUXqZRX+6rtxaC0LpqroLBo2k/K+cxJhk+Rr3kFkNyvhc2jfemll4RwnTx5UtJn0JQUmmpCgUJ5PQwNGjQQ00/ISjUkIkKboaN+/fr6NorQJZfxsmXL8M0335jMOsUwjHXyKFOJ5UeipJf/InRWZouXAVdvk2OZfWr1QW0viecr73mabdu2FWJJQkmWBM3hs5akBmWJLiyayCucpR0WPXHiRBw+fFi4Zym4ZsWKFUKMyIor7Jh27doJa48eikjgaLoJjVVSoI5CIf06GzduLKavkBeCxjvpPFROjoKRyI1LgUnEtGnTsHHjRnGN586dEy5fsogZhrEN/jwajYcZStT2rYCejQqeW6nn/jXgcrh2uc3rRpsuJV7Czls7xf3z9WbG2yxWNCnCktx+JJYXLlwQY1J0c6QbbkZGBqwFSqFH/aAx2tKEwp5pWomfm59RO1mgpTXdhKB+0RQQ+r1orHnmzJnCihsyZEiBx9DYJAkcud1pqkrHjh2FgE2YMEFYruYmJCCPBE1VGTt2rBBRCkR69tln8dtvv+n3IdcuTVGhIKPOnTsLYaYxToZhrJ9slRq/79emzKOIWblcgrfsyC/ampn1egE+9Uxamb1r9kbtiuVjZRIyjcTK0QMGDBBWB01wN4ySpOAN3bw6ygRD1oq1kJKSIsbXqOxZ3hSB9BBAFhLNbyRBeRxo+gklDy7LsGhboyR/D4ZhSp/1J29j4qpIMSdz/+RucHEs4p6XngTMDwayU4FhG4A63YyszIF/DxRW5l/P/IW63nXLVA+KFQgUGxsrxrsoYMSQ9u3bi7GrKVOmCMuCUrExxpBAhlYpXauWYRjGUtBoNPhlj3aaycgONYsWTF3NTBJM30ZAbePpb7+cJgsUeCLoiVIRTHOQLJqULaagYBSa2E7jYGSNMgzDMPbNvisJuBj7EG5OCgxtIyHmRaXMcc3mJDMw0JorD67gvyjtfPHXm5ffWGaxqpzExcUVug+NSzEMwzD2za85yQwGh1aHl5tj0Qdc+gdIjgZcKwHNBpkcyyQrs563safTokVT4tAnwzAMY8ecvZOM/VcToJDL8GpHickH9DUzRwGOuXPSrz64alFWJsGlwYqAHxYsA/4dGMY6WLhPa2U+1TQAgd4SUn/eiQCiDwFyRyD0tXxjmZSru2eNnqjvnTu/uzwxK+8dTReg1GmFQVMLbAEqc0VQ+jgas2XKF10aP93vwjCM5XH7QRo2n44xL5nBkZz0nk2eBzxzq5dcS7qGf2/+a1FWptmiSblLC5vkToFCtiKa1E8qpKwbx3VzMy8rD1NyFiYJJv0O9HuYk2SBYZiy5ff9N6FSa9ChbmU0qaZNl1koKTHA2b9MVjPRWZndq3dHg0oNYJWiSbUW/fyMJ+rbMrp6kUUFQDGlDwmm7vdgGMbySE7Lxp/HoqUnZieO/aatmVmjHVA1tyzh9eTrCL8RbnFWplmiaY9WFvWZkpbTg0J2dnZ5X47dQi5ZtjAZxrJZdiQKaVkqNKzigc71fIo+IDsdOP67SSvz19O/CiuzW/VuaFS5kXWKpj0HYtANm2/aDMMwpslUqrD44E39WKYkI+v0aiA9EfCqATR4St98I/kGtt7YapFWplnRs1QOrLAgoL/++kvkEGUYhmHsiw0n7yD+YSYCvFzQr3lVM2tmjjWqmUlWplqjRtfArgiubFzi0OpEc+nSpRg4cCBefvllHDmirSu5c+dOUeh42LBhIrE3wzAMYz+o1Rp9MoNRHWrBUSFBVq7vBuIvAE7uQMgwffPN5JvYcmOLWH69heVZmWaJ5ty5c0Wi9ps3b2LTpk3o3r27KD1FlTOo3BMVpKaixNZAWVU5YRiGsXV2XIzDtfhUeDg74MWw6mbWzBwCuORG2S48s1BYmZ0DO6Nx5cawatGkclELFy4UEbRUXzE9PV1UOKFaiJSs3dtbQkVuC4HqSp4/fx7Hjh0r70thGIaxan7de028v9y2BjxcJMyjTrgCXKH5lzKtazaH6JRo/HP9H7E8rrlxYJBVimZ0dLSwLolOnTqJiMYZM2agQoUKpXl9DMMwjIUSEf0Ax24+gKNCJlyzZiUzqN8HqFzHaCxTpVGhY7WOaOLTBFYvmpmZmUZ1DKmAcKVKlUrruhiGYRgL59ec8l/PtqgGf08JdW7THwCnVuSbZnIr5RY2X99s8Vam2ckNpk6dKjLjEFQ3c9asWaJopyHz588v2StkGIZhLAaVWoOjNxJx7m4yws/FmpcyL2IJkJ0G+DcBanU2GsskK7NDtQ5o5tvMNkSTyn5dunTJqPj09evapwx7ToDAMAxjL4SfjcGMv88jJjlD3+bsIMf1+Eeo7+8hoWbmr/lqZt56eAubrm2yCivTLNHcvXt36V4JwzAMY9GCOW5ZBPKmuclUqkX7gqEh6NMkN+F6Pi7+DaTcBtx8gCYD9c2/nflNWJntq7ZHc9/msHS4NBjDMAxTpEuWLExNIfvQdtqvQA79pH0PfRVw1I5/3nl0B5uuWo+VSbBoMgzDMIVCY5iGLtm8kFTSdtrPJLePA7ePamtmtn5V37zw9EIoNUq0DWiLFn4tYA2waDIMwzCFEvcw4/H2O5yTzKDpQMDDXyzefXQXG69utCork2DRZBiGYQrFz8Ol+Psl3wHOb8g3zYTGMsnKbBPQBiH+IbAWWDQZhmGYQgmrVUkkYy8IioOl7bSf6ZqZSiCoIxCgDfSJeRSD9VfXW52VafY8TR1JSUk4evSoKM6sVquNtg0fPrykro1hGIaxABRyGaY82RDv/Hkq3zbdRMPp/YLFfkZkpQEnFpm2MtVKhFUJQyv/VrBp0fz7779FkvZHjx7B09PTaG4mLbNoMgzD2B73H2WJd4UMUBkEyVbxchGCaXK6yek/tVmAKgYBDZ4UTbGpsfjr6l8WWy+zxEXz3XffxahRo0SFE112IIZhGMZ2ychW4ec92sTsn/Zvgto+7iLoh8YwySWbz8IkyAupr5n5OiBXGFmZrf1bI7RKqO2L5p07dzB+/HgWTIZhGDth1bFbiHuYiapeLnihVXU4OUipmbkTSLgMOHkALYfmWplX/rLKscxiBwL17t1blAdjGIZhbJ9MpQoLdmutzHHd6koTTEJnZVKRaRdPsfj72d+Rrc5GiF+IVVqZxbI0n3rqKbz//vuiHmXTpk1FiTBDnnnmmZK8PoZhGKYcWXP8NmJTMlDF0wWDWgdKOyj+EnB1uzZMKGyMaIpLi8O6y+vE8hst3rDaXOVmi+bo0aPF+6effppvG30JKpWqZK6MYRiGKVeylGq9lfl6l9pwdtCOS0qumdnwKaBSLb2VmaXOElYmRc1aK2aLZt4pJgzDMIxt8lfEbdxJSoevhzNeDKsh7aC0RODUSqNpJmRlrrm0Rh8xa61WJsHJDRiGYZh8ZKvU+HH3VbE8tnNtuDhKtDJPLAaU6UCVpkBQB9G06OwiYWW28G0h8sxaM8USzT179qBfv36oW7eueNE45r59+2At/PjjjwgODkZoqHUORDMMw5Q2G07ewa3EdPi4O2FImyBpB6mygaMLtctt3xQ1M+PT4rHm8hp9xKw1W5nFEs1ly5ahZ8+eYsoJTT2hl6urK3r06IEVK1bAGnjzzTdFINOxY8fK+1IYhmEsDiVZmbu0VuboTrXh6iTRyjy/EXh4F6jgBzR5XjQtOrcImapMNPNthnZV28HaMXtMc/bs2fj8888xceJEfRsJ5/z58zFz5ky8/PLLJX2NDMMwTBny9+m7uHk/DZUqOGFoW4lWpuE0k9DXAAdnJKQn6McybcHKLJalef36deGazQu5aG/cuFFS18UwDMOUA1RI+vudWivztU61UMFZom116xhw5zigcAJajxJNi88uRoYqA818mqFDVe34pt2JZvXq1bFjx4587du3bxfbGIZhGOtl8+m7uB6fiopujhjerqb0Aw//qH1vOghw98X99PtYdWmVTUTMPnbuWXLHnjp1Cu3btxdtBw4cwOLFi/Htt9+WxjUyDMMwZYDawMp8tUMtuEu1MpNuAec3aZfbapOw/3HuD2FlNqncBB2rdYStYLZojhs3DlWqVMFXX32F1atXi7ZGjRph1apVePbZZ0vjGhmGYZgyYOvZWFyNewQPFweM6GCGlXlsIaBRAbU6i6kmZGX+eelPsWlcC9sYy3yseprPPfeceDEMwzC2ZGVeEcujOtSCp4txitQCyUrVzs0k2r4h3v44/wfSleloXLkxOlXrBFuCkxswDMMw2Hb+Hi7GPhQuWRJNyUSuBDKSAe9aQL3eeJDxAH9e/NPmxjJ1sGgyDMPYORqNBt/t0FqZr7SvCS83iVammmpm/pybMk8uF2OZZGU2qtQIXQK7wNZg0WQYhrFzdlyIw/mYFLg5KfBqRzOszKvbgftXAGdPoMXLSMpIwsqLK21qXmZeWDQZhmHs3crMGcukKSbeFZykH3z4J+17yHDA2QNLzi9BmjJNWJldq3eFLVKsQCBDqBTYmTNnEBQUBG9v75K5KoZhGKZM2H0pHqdvJ8PVUSGSGRSJWgVEHQTuRgDXd+lrZpKVueKiNpXq2OZjbdLKLJZoTpgwQRSffvXVV4VgdunSBQcPHhS5aDdv3oyuXW3z6YJhGMaWsv4cvZGIuJQMvZU5tG0N+Lg7F37g+U1A+GQg5W5um4MzEBOJJRk3kZqdigbeDdC9enfYKmaL5tq1azF06FCx/Pfff4vUeRcvXsTSpUvx8ccfi0QHDMMwjGUSfjYGM/4+j5jkDKP2un4eRQvm6uHk0DVuV2Ygee0rWFGrts1GzD7WmGZCQoJIbkBs2bIFL7zwAurXr49Ro0YJNy3DMAxjuYI5bllEPsEkpqw7LbYX6JIlCxN5BDOHpZ7uSFVnol7Feuhew3atzGKJpr+/vyirRa7Z8PBwPPHEE6I9LS0NCoXE8jEMwzBMmbtkycI0LXtaaDvtlw8awzR0yRqQLJdhuZfWSn09oAvkMtuOLzW7dyNHjsSgQYPQpEkTYYJTbU3iyJEjaNiwYWlcI8MwDPOY0BimKQtTB0klbaf98vHoXoHHLff0xCO5HHWzstDTpSpsHbPHND/55BMhmLdu3RKuWWdn7cAxWZlTpkwpjWtkGIZhHpO4hxnF38/d3+S+KXIZlnnmWJkPkiH30A7d2TLFmnIycODAfG0jRowoiethGIZhSgE/D5fi7xfUHvAIAB4aj3ku9/TAQwVZmdl4wqGSdj8bR5Jofvfdd5JPSGXDGIZhGMsirFYlBHi5FOiipXjXKl4uYr98yBWAfxMhmioAES7OiHZwwCJPT7F5bFIy5E/9ot3PxpEkml9//bXRenx8vAj8qVixolhPSkoS8zT9/PxYNBmGYSwQhVyGEe1rYu7Wi/m26SaITO8XLPbLR/QRkTJvu5sr5vpUxj1FbjiMQqOBvN1bQPAzsAckBQLRXEzda/bs2WjRogUuXLiAxMRE8aLlkJAQzJw5s/SvmGEYhjEbiordfFobAeviaHzrJwtzwdAQ9GkSkP9AZSaw6W1sd3PBJH9fI8EU55XJ8N61VdgetR32gExDiQfNoE6dOiLBQcuWLY3aT5w4IcY6SVithZSUFHh5eSE5ORmeOW4GhmEYW2TRgRtiSgkVmP5vYhfcSEgVQT80hkkuWZMWJrFzNlR7P0fvGtVxT2F6Hxlk8HfzR/iAcCis1EUrVQ/MDgSKiYmBUqnM107zNu/dKzgsmWEYhikfYpMz8NW2y2J5cp+GwrKkV9EHngX2zxdjmAUJJqGBBrFpsYiIi0BolVDYMmbP0+zRowfGjh2LiIgIIytz3Lhx+jmbDMMwjOXw6eZzeJSpRMsaFfFyWA1pB1EWoE1vA2ol4qu3lnRIfFo8bB2zRfP3338XafRat24t5mjSKywsTGQK+u2330rnKhmGYZhisetiHLaciRXu19n9m0JekBs2L4cXaCuZOHvBt620AE9fN1/YOma7Z319fUXO2cuXL4tE7QRlAqL8swzDMIzlkJ6lwtSNZ8XyqA41EVxVYuxG4g1g5yztcq+ZCKn1BHxP+CI+Pb7QMc0QvxDYOsWup0kiyULJMAxjuVDZr9sP0lHVywUTekq8X1Ns6N/jAWU6ULOTKDCtkMlQzb2aSdEkwSQmh0222iCgUhVNCvhZvHgxduzYgbi4OKjVaqPtO3fuRFlC6fyGDRsmrsXBwQFTp04V6f0YhmHsmcv3HmLh3uti+ZNnGqOCs8Tb/cllwI29gIML0O9bQCbDjugdOBV/CnLIUdGlIhIzcvPTkoVJgtkzyD5iWswWzXfeeUeI5lNPPaVP2l6ekFB+8803Yu5obGwsWrVqhb59+6JChQrlel0MwzDlhVqtwcfrz0Cp1uCJYH/0aiwxJ+zDWGDbx9rlbh8DlesgOTMZsw5rXbUjm4zE2y3fFlGyFPRDY5jkkrUHC7PYovnnn39i9erVQpgsgYCAAPEiKEDJx8dHJFxg0WQYxl5Ze+I2jt18ADcnhbAyJbPlfSAjGQhoAbR9QzR9efxLJKQnoKZnTYxrMU4IpK1PKynR6FknJyfUrVu3xC5g79696NevH6pWrSqs1g0bNuTb58cff0TNmjXh4uKCNm3a4OjRoybPRVNfyH1cvXr1Ers+hmEYa+L+o0x8tvWCWJ7Ysz6qVXSVduD5TcCFTYDcAXj2B0DhgIN3DmLD1Q1i3PLTDp/CWaGtamXPmC2a7777Lr799luYmUioQFJTU9G8eXMhjKZYtWoVJk2ahOnTp4u5obRv7969xRimIWRdDh8+HL/++muJXBfDMIw18tmWi0hKy0ajAE+M7FBT2kHpD4At72mXO0wAqjRFWnYaZhyaIZpeavgSWvoZZ4GzV8x2z+7fvx+7du3C1q1b0bhxYzg6Ohpt/+uvv8w635NPPileBTF//nyMHj1aFL8mfv75Z/zzzz9ivqiufmdmZib69+8v1tu3L7g0De1HL8O0SQzDMLbCoWv3sS7iNsXuYPZzTeCQJ09sgWybqi00Xbke0Pl90fRNxDe4m3pXRM2+E/JO6V64LYsmVTZ57rnnUBZkZWUJl+uHH36ob5PL5SLz0KFDh8Q6WbyvvPIKunfvLqJoC2POnDmYMUP75MQwDGNLZCpV+N+GM2KZsv6E1PAuPNtP1EGtUFKNzJNLte3PfA84uiDiXgRWXlwpmqa1mwY3R7cy6YNNiuaiRYtQViQkJIgxSso2ZAit6xIrHDhwQLhwmzVrph8PXbp0KZo2bZrvfCS+5Oo1tDR5/JNhGFuAppdci0+Fj7sTPujdsPCxy/DJQIq24omeOj2AoHbIUGZg+sHpoum5us+hfVXbLyxdJskNqKbmpUuXxHKDBg1EpqDyoGPHjvnmihaELu0fwzCMLRF1PxXf77wqlqc+HQwvN+NhMyPBXD1cpFjPx7WdYvuC9Gu4mXITvq6+eC80Z5yTKb5oUuDO22+/jSVLlujFSqFQiCCc77//XhSjLilo+gidO2/1FFqn6SUMwzD2XB/z6I1ExKVk4Lf915GpVKNjXR8807xqwS5ZsjBNCWYO5/6bgj+8ncTy/9r+D55OXDLxsaNnyb25Z88e/P3330hKShKvjRs3ijaKrC1JaHoLJSug7EM6SKhpvV27diX6WQzDMNZC+NkYdJy3Ey8tPIx3Vp3CmTvaoEZKZFBgwhkaw8zrkjUgGxpMc1VBpVGhT80+6F6je2ldvn1ZmuvWrRNFqLt27apvo0QHrq6uGDRoEBYsWGDW+R49eoSrV7VuBYKKWJ86dQqVKlVCjRo1hEiPGDFCVFWhaiqU/YesXV00bXGg6S30ovFShmEYaxPMccsiTNqLn2w6B39PZ/Rpok34YgQF/RTC/1X0xGVnJ1RUuOLDNrnBl8xjimZaWlq+wBzCz89PbDOX48ePo1u3bvp1XaAOCSWl6xs8eLAYP502bZpIk0fp8sLDw01eg1TefPNN8dJV6mYYhrEWl+yMv88X4mCF2P5EcBVRCswI94LvmVccHfFLRe29cEq9F1HJpVJJXbLNIdOYmaWAilBXrlxZjGlShh4iPT1diBwlGNi+fTusBZ1oJicnw9OTffcMw1j+PExyyRbFytFt0a5O5fxjmvMb5bM4yd82LMAfZ1yc0SVLg+9HnYJMUewYUatFqh6Y/c1QNiDKyBMYGCiy8xCRkZFCQP/999/Hu2qGYRimQOIeZhR/P40acHYXoklCGeHijHiFAiecnYVguqvVmBo6xS4F0xzM/naossmVK1ewfPly/VzJl156CUOGDBHjmgzDMEzp4OfhUvz9ds4E7l/Ddo+KmOvtgXsKY/dtX/+28G9ReIIYppjzNGlaCaW2YxiGYcqOsFqVUNHNUeSWNQXJYBUvF7GfEVe2Awe+xXY3V0zy8TQ5Jrom/hjaRW23m7qYZTblhFLRUd7XvFDbvHnzYA1Q5GxwcDBCQ+23vA3DMNZZwSRbaTqZi85unN4v2DgIKCUGWD9WuGTnBgQWGkQ07+g8qGjskyk50fzll1/QsGH+FE2UvJ2SqVsDFDl7/vx5HDt2rLwvhWEYRhIUs/nButNIzVIhsKIrqngau2DJwlwwNMR4ugkJ4F+jgbQERFRthHvqzILPDw1i02JFgWmmBN2zNO1DV/TZEEqjFxMTY+7pGIZhGAksOxyF3Zfi4eQgx+8jQ1HH112bEehhhhjDJJdsvmkme78Ebu4DHCsgvs1Y4NT8Ij8nPi2+9Dphj6JJCc4pSXqtWrWM2qmNCkkzDMMwJcu1+EeYvUVbWHpKn4ao7+8hlvNNKzHk5n5gz1zt8tPz4etfR9Jn+bqVTx5xmxVNCgCaMGECsrOzRTkugtLaffDBByWeRo9hGMbeyVapMXHVKWRka3PLvtJeQmHp1ARg3WvaaSYthgDNX0SIWgU/Nz/EpcWZPEQGGfzd/BHiF1LynbBn0Xz//fdx//59vPHGG6LeJUFzNCdPnmxU95JhGIZ5fL7fcQWnbyfDy9URX77QHPK8Lti8UCGNDeO0dTJ96gN9vxDNCrkCzXyaYXv0dpOCSUwOmyz2Y0pQNCkZMEXJTp06FRcuXBBzM+vVq2dVJbc49yzDMNbAiagH+GGXNjf3rP5NRLBPkRz+EbiyDVA4AwMXAU4VRPO1pGvYfXu3WK7oXBFJmUn6Q8jCJMHk6SalkEZPByVZv3btGjp37iyEk05TYHZ9C4XT6DEMY6mkZirR97t9iLqfhv4tquKbF1sWfdDt48DvvQG1Enj6a6D1KNFM9+eR/47EiXsn0DWwK77u+jVOxp8UQT80hkkuWXu3MFNKK40euWapmsmuXbuESFJ2oNq1a+PVV1+Ft7c3vvrqq8e9doZhGLtn1j/nhWBW9XLBjGebFH1AehKwdqRWMIP7A61yK0FtvLZRCKarg7aCiYPCAaFVeJ56mczTnDhxIhwdHREdHW1UcJqqkVD1EYZhGObx+O/8Paw8egvkvPtqUAsxnlko5DD8ezyQFA1UDAKe+Y7G0sSmpIwkfHVca8y83vx1VHXnWQ6Pg9mW5rZt20RidkrYbgiNa0ZFRT3WxTAMw9g78Q8zMWXdabE8ulPtgqeVUOICKixNVUvuHAfObwTkjsALiwCX3JKHX0d8LcYv61asi2HBnFu2zEWTCkAbWpg6qCyYNQUDMQzDWBo09kiCeT81Cw2reODdXvVN73h+ExA+GUi5a9ze9AWgWiv9Krlk/7ryl1ie1m4aHElUmbJ1z3bq1EnU0tRB45pqtRqff/65UTFphmEYxjzIJbvjYhycFHJ882ILODsoTAvm6uH5BZOIXKndLuZ3ZmPW4VlieUC9AWjpJyGQiCl5S5PEkQpRHz9+XMzTpKQG586dE5YmZQViGIZhzOdmQipmbj4vlt/v3QANq3iadsmShVlY2vXwKUDDp7Dk/BJcTboKb2dvTGw1sRSv3L6QF6ee5uXLl9GxY0c8++yzwl37/PPP4+TJk6hTR1qapvKGq5wwDGNJKFVqTFh1CunZKrSrXRmvdjROU6qHxjBNWZh6NEDKHdy59Dd+jtQW0Hgv9D14OeeOcTLlNE/TFuB5mgzDWALfbr+Cr7dfhoeLA8IndEa1iq6mdzyzFlj3aqHnohv6W817YG/KFbT2b43fe/9udXPoLVkPzLY0aVrJ/v37jay2Fi1a4OWXX8aDBw+Kf8UMwzB2yKlbSfhu5xWxPPPZJgULJuHuX+T5dri5CsF0kDtgarupLJgljLw4uWdJkYkzZ85g0qRJ6Nu3L27cuCGWGYZhGGmkZSlFMnaVWoOnmwXg2RZFzKHMSit0c6pMjjk+PmJ5VJNRqO1VuyQvlylOIBCJI40HEuvWrUO/fv3w2WefISIiQognwzAMI43PtlzAjYRUUVB6dv+mhVuF0UeANSMMGmhfw9E1GX709kKcQoZA90CMbjq6NC/dbjHb0nRyckJamvZpZ/v27ejVq5dYrlSpkt4CZRiGYQpn18U4LDscLZapeomXWyFzKGPPAiteAJTpQN2e2kTsngFGu1zwrorlXtqxuP+1/R9cHCQkd2dK39KkqFlyw3bo0AFHjx7FqlWrRDtF1ObNEsQwDMPk5/6jTLy/Vpv1Z2SHmuhYT+tSNUnidWDpc0BGMlC9LTBoKeDkBgQ/q88IpKrgi0/PL4D6/jn0qdkHHap1KLvO2BlmW5o//PADHBwcsHbtWixYsADVqlUT7Vu3bkWfPn1K4xoZhmFsBpqw8OFfZ5DwKBP1/NwxuU/DgndOiQGW9AdS4wD/JsDLq7SCCYAKGx5zdcGWCm74MuEwzt4/B3dHd7wf+n7ZdcYOMdvSrFGjBjZv3pyv/euvvy6pa2IYhrFZ1py4jW3n78FRIRNZf1wcCyjJlZYILHseSIoCvGsBQ/8CXCuKTdujtmPu0bm4l3bP6JBeQb3g5+ZXFt2wW8y2NG0BTm7AMEx5EH0/DTM2nRPLk55ogMZVC0g6kJUKrBgExJ0H3KsAwzcAHv56wZy0e1I+wSTWX10vtjOlByc34OQGDMOUATStZPAvh3A86gHCalbCyjFtoZCbiJZVZgIrBgPXdwEuFYGRWwF/7YwFlVqF3ut6mxRMQgYZ/N38ET4g3O6LSltMcgOGYRjGfH7ec00IpruzA74a1Ny0YFJu2b/GaAXTsQIwZK1eMImIuIgCBZPQQIPYtFixH1M6sGgyDMOUMmduJ+Pr/y6L5U+eaYzqldxMF5LePBE4v0FbF/PFZUB14yGk+LR4SZ8ndT/GfFg0GYZhSpGMbBUmrDoJpVqDJ5tUwYAQ7YyDfOyYAUT8AcjkwIDfgDrd8+3i6+Yr6TOl7seUURHquXPnYseOHYiLixO1NA25fv16MS6DYRjGNpm79SKuxafCz8MZnz1XQNafA98C+3NmIDz9DdC4v8lzJWcmF/pZujHNEL+QErl2pgRE87XXXsOePXswbNgwBAQEcDJghmGYAth7OR6LD94Uy58PbAbvCk75dzrxB/DfNO1yzxlAK8NUebksv7Ac847OK1QwiclhkzkIyJJEk5IY/PPPPyIjEMMwDGOaB6lZeG9NpFge3i4IXRuYmD95fiOweYJ2ucM7QMecZQPUGjW+Ov6VKCpNvFD/BbQJaIMvjn1hFBREFiYJZs+gnqXWJ6YYount7S3yzDIMwzCmoZl8H284g7iHmajjWwEfPtko/07XdgHrXgM0aiBkuNbKzEOGMgMf7f8I/0X9J9YnhEwQ1UvIw9ezRk8RJUtBPzSGSS5ZtjAtUDRnzpyJadOm4Y8//oCbm4kIMIZhGDtn/ck72HImFg5yGb4Z3BKuTnnE7PZx4M8hgCpLm0OWxjHzDHUlZiRi/M7xiIyPhKPcEbM6zELf2rmVpEggQ6twghaLF82vvvoK165dg7+/P2rWrAlHR+PM/FQijGEYxl65/SAN0zdqs/5M6FkPTQPzZP2JuwAsGwBkpwK1uwHPLwTyWIjRKdEYt30coh9Gw8PJA991+w6tq7Quy24wJSWa/fubjupiGIaxdyjrz6TVkXiYqURIjYp4vUsd4x0e3MypWJIEBIYCg5cBDs5Gu5yKO4W3d76NpMwkVHOvhp96/ITaFbmYtNWK5vTp02ELuWfppVJRnQCGYZiSYeG+6zh6IxEVnBT4enALOCgMpsI/vKetWPIwBvBtBLy8GnB2Nzqexi4/3PchMlWZaFy5MX7o8QN8XAspG8aUOZx7lnPPMgxTApy/m4Jnf9yPbJUG8wY0xeDQGrkb05OAxU8D984AFWsAo7YZFZGm2/DS80vx5fEvRSq8roFdMa/zPLg5ctyIpemBJEuTomWpyLSPj4+Ini1sbmZiYmLxrphhGMbKs/6QYD4R7I9BravnbsxK0yZgJ8Gs4AcM22AkmJSE/fNjn2PFxRVifXCDwfgw7EOOhLVQJIkm1cr08PAQy998801pXxPDMIxV8cW/l3D53iP4uDthzvMGWX+UWcDq4cCtw4CzFzBsPVA5d5wzXZmOyXsnY9etXWL93VbvYkTjEZw0xoJh9yy7ZxmGeQwOXE3AkN+OiOXfX2mN7g39DSqWjAbOrgMcXLU1MWu01R93P/2+CPg5k3AGTnInzO40G31q9imvbtg9KSXpnmUYhmHyk5yWrc/683KbGrmCSbbIlve1gil30EbJGgjmjeQbeGP7G7j96Da8nL3wfffv0dKvZXl1gzEDFk2GYZhiMnXjWcQkZ6CWTwX87ymDrD+7ZgPH/09khMVzvwD1clPbRdyLwPhd40Xy9UD3QCzouQA1vWqWTwcYs2HRZBiGMWMeJk0piXuYgSv3HmJT5F1RTHr+oOZwc8q5nR76Edj7hXb5qa+ApgP1x4ffDMfH+z5GljoLzXya4bvu36Gya+Vy6g1THFg0GYZhJBB+NgYz/j4vLEtD+jSugpY1vLUrp1YA/36kXe7+PyD0VbFIoSOLzy3G/BPztZuqd8fcznPhSmOdjH2I5tWrV0U6vc6dO8PV1VX8UXDEF8MwtiqY45ZFwFTU5JYzMWJ7H8UJYONb2sZ2bwGd3hOLSrUSc4/OxapLq8T6kEZD8H7r93lKiZVikK5CGvfv30fPnj1Rv3599O3bFzExMaL91Vdfxbvvvlsa18gwDFOuLlmyMAubZvD3xlXQrB0JaFRAiyFAr1kiAXtadhom7JogBJPqXX4Q+gGmhE1hwbQn0Zw4cSIcHBwQHR1tVOVk8ODBCA8PL+nrYxiGKVdoDDOvS9aQJrLrmJc1BzKqWNLwaaDfd0IwE9ITMOrfUdhzew+cFc6Y33U+hgUPK9NrZyzAPbtt2zb8+++/CAwMNGqvV68eoqKiSvLaGIZhyhUadjp8/X6B2+vI7uAPp7lwl2Ug3qcNfAf8H6BwwPWk63hjxxu48+gOvJ29RcBPC78WZXrtjIWIZmpqqsk6mpQ+z9nZOFs/wzCMNZKWpcTGU3ex5FAULsSk6NvlUCNMfhF+SIIKMnzsuAyVZI8Qqa6NjJ4L4evogmOxx/DOrnfwMOshanjUEFNKanga5KFl7Es0O3XqhCVLlohi1AQF/6jVanz++efo1q0brAGucsIwjCluJqRi6eEorDl+CykZStHm7CCDXCZHZ9UhTHdcgqoy4/zaMWpvTHaZhn/q18A/1//B1ANTka3ORnPf5iJpgbdLTmQtY59p9M6ePYsePXogJCQEO3fuxDPPPINz584JS/PAgQOoUydP/TgLhtPoMQyjVmuw+3KcsCp3X4rXt9eo5IZhbYPwQutA3Ni3Es0PjhftcoNJArq758l23yKimhLfRnwr1p8IegKfdfwMLg4uZdwbxuLS6DVp0kRUPPnhhx9EEvdHjx7h+eefx5tvvomAgNzM/QzDMJZMUloW1hy/LSzL6MQ0fXvXBr4Y0a4mutT3hZwUUq1Cy3NzoZGJ/D5G0Cy7bMjwd9QXWHtPG1c5IngEJrWeJKxTxvYo1jxNUuOPP/645K+GYRimlDl7JxlLD0Vhw6k7yFSqRZuni4Mo5zW0bRBq+lTI3TnzEXD4JyDlrhBMGtCJcHFGvEIBX5UKDTKzMNnPB/td5GJKyeSwyWIeJmO7FEs0MzIycPr0acTFxYnxTEPIXcswDGNJZCnV2Ho2RrhgT0Q90Lc3CvDEiHZBeLZFNbg65cydTIoGLv8LXA4HbuwDVJmiebubK+ZW9sY9h9zbpoNGA6VMBhe1GvPqvoTuLJg2j9miSXMxhw8fjoSEhHzbKCiIg2sYhrEUYpMzsOJIFFYcvYWER1rxc5DL8GTTACGWrYK8IdOogdvHtSJJYhl3zvgkFfyxXZOCSX4++RIckGDSwObrD5LRPbBL2XWMsZ5AIJqP2atXL0ybNg3+/jllcKwUDgRiGNuDbmlHbiRiyaGb+PfcPZHRh/DzcMaQNkF4Kaw6/JyygGs7tCJ5ZRuQZjAXk8Yiq7cF6vcGGjwJVcWa6L20FcSQpalUoRoNqqiB8OERUDg4lWFPGasIBLp37x4mTZpk9YLJMIxtkZqpxPqTd8R45aV7D/XtYbUqicCeXgFpcLy2DVi/FYg6CKizcw929tKW76rfB6jbE3CrpBfgLdc2456ikLzaMhliFUBEQiRCq4SWah+Z8sds0Rw4cCB2795tVVNLGIaxXa7HPxIRsGuP38bDTO3cSldHBQa09MfomvEIStgC7A0HEi4bH1i5rlYk6UUFohWOIlfsmYQziLwaicj4SJyOP42kzCRJ1xGfljtdhbFdzBZNmmrywgsvYN++fWjatCkcHR2Nto8fr53LxDAMU1qQy3XnRZpbeRP7ruTGVzStrMGkWtHooD4Op8s7gNMGgid3AGq0Ey5X1OsNTeU6iEqJEuIYeXSOeL+adBVqGuM0wEHmAKVGK8aF4evmW7KdZGxDNFeuXCnyz7q4uAiL07AcGC2zaDIMU1o8SM3CquO3hAv2TlI6OVBRRx6DsVUuo5fDKXjFn4DsrEEwoqs3UK+XGJ9MrdEOZ1NvaUUy8usCrciqFaqKbD7N/ZqLQtH1KtbD0xueRlxaHDQmap3QVBN/N3+E+IWUdvcZawwEqlKlihDGKVOmQC637sm7HAjEMNbB6dtJYrrIpsi70CizECq/iL5OkejrHIlKmbeNd/ZtBE29XoiqHoJIBw0iyd1agBVJ1UeCKwdrRTLnZcpi3B61HZN2TxLLhsJJgklQBZOeQT1Lp/OMRemB2aJZqVIlHDt2zCbGNFk0GcZyyVSqRIHnPw5GIepWNLrKI9FDEYFuDmdQQZObwQdyR6TW7IAzgU0R6eaO06m3JFmR9N7AuwEcFcZDTAVBwknFpO+l3dO3VXGrIhIasGBaP6UmmlRP09fXFx999BGsHRZNxpJRqVWIiIsQASZk/ZD7zx6KF99NSsfywzdx7OhBtMo8KoQyRHYFcpn2VkX/jfL0Q2RgM0S6eyEy+wGuJl8vthVpDvb6m9gDKaU15YSSF1BFE6qp2axZs3yBQPPnzy/eFTMMU6hVQ+NmU8KmWJ1Vk5WViQ17fkFcSjT8PGugf5excHJyzl+38vJdHN29CRVv78SLspN4Xx4POAKpMhmOOjshsnINRHpWwmllCpKVqUD6ZYCGNUvAipQKCSRPK7FvzLY0Cyv/RYFAVPnEnixNW3nytJV+2EJfdONneYNOrHH87NeNH2NlwgYkOOTGP/go1XjJpz/GPDsbqffvIHLnaqguhSMk+yTcZJm46eiASGdnRLq44rSHN64iC+o83wVZkY0rNxbi2My3WYlYkYx9k1Ja7llb4nFF01asAVvphy30hQS/97reRtdvKlIzfEC4xT8IkGD+8GCjVu4Mo+xzbjlT76vR99FdnCEr0sVZK5TOLkgxkUigLKxIxr5JYdEsXdG0FWvAVvqR25eJwtWX7yYtk2F+16+L1Rc6n0qjQpYqSxQXFi9Vzrs626j9cfe5l3oPx+4dK/KayHr2c/MTwknzCB3kuS+FTKF/d5Q7apdz9qN3fZtuP2qTOWr3MTgH7We4D70b7qfbJvbLOb9co4EsIxlZKbHos2UQ4kkAC0g9R2NDNDlEk2e7oRWpsyTZimSsSjSpXubixYvFiWi5MP766y/YumgWZQ0QZA2s67dOTMshQdJ9zfSu/1/OslG7qTbDdvF/4zaj9jzHiPMUsC/1Y/yu8UjMMK5Eb0gl50qY23mu0Xko4MLwnIWu0/7kXDPony5gQ7dfoeuGx5k4j26ZRG3Z2cVIVWUUeJN2kTuhY/UuUKqVksVM12Zqfh5jGqr8QQ8q2RKnpLEVydhcIBCdSJfEgJbtHRovK0wwCdrecVVHWDuJmYkY898YWA2mBDOnPUOTje3R20vkY5zkTuLGTlaW4bL+PeflpHDKXVeYbjdcvpt6Fysvrizy84c2GopAj0DxAKB/aZTiQUi3TO9iXZUFZXYalMp08a5SZkBJL1VmzisLKlV2zv5KZGtU4iFECW3ZK7IG6Z1y4hiuqwr4rkXlj4J+hzwMq9AFHwz8wezvn2HKC0miuWjRInz66ad47733xLK18+OPP4pXccuYlXSOSXKF0kOJ7n/a/8uM23NuQgXua9Amdd90ZbqkvJpkNXs6e0IOuf5Yqkqve6fzGm6TQQNREIK8otBaHdp1jXD6Uikm4cbL2abdTu/qnHXtdoh3bRu9Q6PSHqdWG6yrxXq0Og2HHYq2BvtnKdDC0QuOMgc4CaFyMBY6hVOOCDrB0YGWneGocIGjg3POywUOCmfIFE6AeDnkvDtp07TlbZc7ipym2jaDZV17HnFRKbOw89wKxMnzuy3Fb6rRwF8NvBfQDYrMh0D6A+0rLQlITwTSEvO8PwAyk1FcsjQKJMEDDzTuSII7HmhylxM1FfAA9HIT70lwxUO5GxQVPFHT5SDOee8q8vw1veoX+9oYpjyQPKapUCgQExMDPz8/2ArFdc8eu3sYo/4bXeR+Pwf2Q6hbdchIPfSCYfCuuyeKn0BTyDtMt0s61vDdYH/qR+IFjMq+VmQ/fs/yRKijN6DK0r6U9J6ZZzkbUGYaV44oQ465OGNUQNGVd36PuYfQDG1dRYtAL7SOWiHVaLBdliZqNxIaEwE08+MS0DPNYK6FRFJl7kjOEbsElTsewB1Jmhwh1C3nCCTtR++pcBGPXx4uDqK0lq94ucDX3Rl+ns7iXdvmLLZ7uzlBLpeJaSa9l4bgvkJWoPj7qDQIHxaRb/oJw9jEPE07jhfKR0hGJvyVSsQpFAVbAyoV2u77EZYc30iZMv2rVy2yHyF3zhb/Q4RF5QRQnUGFc8573mXa7ixhOec4o/PRuiOax12C/7VFRfaleZuJgG8DrciTwIsHAXo3WNa3K3MfFNQGy2a1G5zb1AMF7Ucvg009c4RxbmVv3HPI/SdK1z/5/gMhmOkKDyQ5+iIZHrivdke80g2x2a5iOa9FSMvJqABVnr9GR4XMSPRIDGvplvOIooujeX/JJIQ0rYSiZ+m7NyX+L/r0Z8FkrA6zkhsYJme3ZxSp8Zhy/4GwBgq6IdDNTVGtFeBVPccFJyvgXRwFjSgAL4NKAyjVEO8qCnDRLasBZc47tdM+Yj+1JqfdoE0DZKtoPaeNzqnSIDvnXbufBr7qOEy5f7LIfvyS3Q8XNEHIgiOy4KB9aRyRnbOcmdOuljlCKXNEtswRarmjWIdaDgVkUGhkkCtlkGfJoJDLQDEiChm5d2lZJpbp42mb2C62Qb+sbxP7atvp75GOo+UHqT4Y8+BXzPJXFNiX0QlKvKLqDO8Y15wgJQo2ooAi7UOhOt+6Bmq1ti1335ztKHwfsUxBTXJALdNAowDUwq2sgkKTLd4dNNliWaFRQgElHDRKNFZfwizFQiGM3dLSEeHijHiFAr708JKRqZe9kenv4HBqsMm/z4pujnqrsKmRJeiSK5DuzmK/0vw3TfMwsRE58zRzP4csTBJMsZ1hbNU9S1GghgFBBZGYWHAkps1Ez17fC8WSftju5prPGqiiVOqtgQU1v8VFl+bIyFYhU6kW7xnZav16Jq3r21XiZlyWyKHGfufxOOuWjs998vfj/YQHaJLmho6Z30ItRiUtm97yoxjg9Uu+vpBX4IOEB1iXPBb/qsNgyeh+kypIFA8NeaG/kVhUxgeBSxFay8/INUrvld2d4OygsLqMQAxjk2n0ZsyYwdGzAI6qGiJIUwndUxNNWgM0hHkXlfHFxcpQ426xPsPFUS5ufvROrjEXBwWcaVn3Tm2OCjg70LK2PXdde5yz0T50DuPjzt1NwYw/h2NB2jfoFp2OU665/WiRrrVqxmWPxfLR7dG2diVhzeqsMVomi5ficbQWsc4Ky1nOaddaYtp9qZ0e0XTHinZT59RZ2HnPmdNueE51zvHX4h5hyWEAycCitCWIdU3V96VKujtmZWsFc3jbINT2rSAe/kiUtO/aZXqnZ0KtRatbN9iW422RG+xT4PEG589tK3gf3bbIW0mYsWY4Fjh+I/plKJy6h6oZ2cPwZveGaFenMqwBEshBT3DJQMY2MEs0X3zxRZsKBCoucanZWJytvbGRQBoGlhje2Po2q4YW1StqxSuPYOmEzZQg0vaycIVXr+SGWR6d8cZDYJrjEoQazNck0f80exhOe3RGWK1K4nocTGRqsRRIVP+7cA/bksPwX2ZrhGVfhB+SEIeKOKpuKOJ5A7xcMP2ZxsKda6nQbzI3PPc3qYrc3yQ2z2/CMIwFiyaPZ+ZCY0NktYzLnoDpJm5sJJi0fWWbIIu2Bkg8pvcLxrhlGUJoqEahTmiOqRsKl+yCfsEWLTL5+xIhBPKwOne8T3f1062gL7b0mzAM7H1MMzY21qYszeJnBNKg47ydiE3OgAxqhMnzWzVVvFywf3J3q7i5hZ+NwYy/zyMmOUPfJqyyfsHo0yQA1oSt9MVW+sEw1gLnni3l3LN0UyOrhjD8AnUSuWBoiFXd3OhB4OiNRMQ9zBCWNLn/rEHwbbkvttIPhrEGWDTLoMoJWwMMwzC2QakVoWZyIWF8IrgKWwMMwzB2AovmY0ICacnBPgzDMEzJYfkz1hmGYRjGQmDRZBiGYRiJ2LV7VhcDRQPADMMwjP2SkqMDRcXG2rVoPnz4ULxXr169vC+FYRiGsRBdKCxdrF1POaGqE3fv3oWHh4fIeBQaGopjx47ptxuuF7a8Y8cOIby3bt0q1tSVvJ9rzj6m2gvrR0F9KYl+SOlLYdvN7UtB/aB3emq0lt/EFv+2SrofUvrCf1vS+qFb5r8tLbrzkRSSYFatWlUk8ykIu7Y06YsJDAw0KrRt+KUbrktZpvfi/Gh5P9ecfUy1F9aPgq6/JPohpS+FbTe3L0X1w1p+E1v82yrpfkjpC/9tSetH3mVr6Edp/m0ZnkNKQRIOBDLgzTffLHBdynJJfa45+5hqL6wfedd1yyXRDynnKWy7uX0pz34Utk9J9EPqNRT3Gkv7b6uk+yHlPPy3lX+d/7aKxtxz2LV71lIyC1kKttIPW+oL98PysJW+cD+KB1uaJYCzszOmT58u3q0ZW+mHLfWF+2F52EpfuB/Fgy1NhmEYhpEIW5oMwzAMIxEWTYZhGIaRCIsmwzAMw0iERZNhGIZhJMKiyTAMwzASYdEsZTZv3owGDRqgXr16+O2332DNPPfcc/D29sbAgQNhrVCqra5duyI4OBjNmjXDmjVrYI0kJSWhdevWaNGiBZo0aYKFCxfC2klLS0NQUBDee+89WCs1a9YUf1f0u3Tr1g3Wyo0bN8T107+Tpk2bIjU1FdbIpUuXxG+he7m6umLDhg2PdU6eclKKKJVK8Ue3a9cuMfm2VatWOHjwICpXts6i1bt37xa5Gf/44w+sXbsW1khMTAzu3bsn/gHFxsaK3+Ty5cuoUKECrAmVSoXMzEy4ubmJGxoJ5/Hjx632b4v4+OOPcfXqVZFH9Msvv4S1iubZs2fh7u4Oa6ZLly6YNWsWOnXqhMTERJE0wMHBurOuPnr0SPw+UVFRj/XvnS3NUuTo0aNo3LgxqlWrJv4RPfnkk9i2bRusFbLQKLm9NRMQECAEk6hSpQp8fHzETcHaoHyZJJgEiSc9+1rz8++VK1dw8eJF8W+EKV/OnTsHR0dHIZhEpUqVrF4wiU2bNqFHjx6P/YDMolkIe/fuRb9+/UTWe6qCYsqs//HHH8XTi4uLC9q0aSOEUgdVUCHB1EHLd+7cgTX2xVIoyX6cOHFCWGzlURquJPpBLtrmzZuLogPvv/++eAAoD0qiL+SSnTNnDsqTkugHHUdWGlXOWL58OayxH/QAQw/5dI6QkBB89tlnsIV/76tXr8bgwYMf+5pYNAuB3F50U6IfxRSrVq3CpEmTRAqniIgIsW/v3r0RFxcHS8NW+lJS/SDrcvjw4fj1119hrf2oWLEiIiMjxfjTihUrhNvZGvuyceNG1K9fX7zKk5L4Tfbv3y8exsiqIbE5ffo0rK0fNKy0b98+/PTTTzh06BD+++8/8bLmf+8pKSliaKxv376Pf1E0pskUDX1V69evN2oLCwvTvPnmm/p1lUqlqVq1qmbOnDli/cCBA5r+/fvrt7/zzjua5cuXa6yxLzp27dqlGTBggMYSKG4/MjIyNJ06ddIsWbJEYwk8zu+hY9y4cZo1a9ZorLEvU6ZM0QQGBmqCgoI0lStX1nh6empmzJihsfbf5L333tMsWrRIY239OHjwoKZXr1767Z9//rl4lTd4jN+E/q0PGTKkRK6DLc1ikpWVJZ4oe/bsaVSfk9bp6YwICwsTQQHkkqVB6K1bt4qnIGvsizUgpR/0b++VV15B9+7dMWzYMFhrP8iqpKAsgqo7kBuLorStsS/klqWo5ps3b4oAoNGjR2PatGmwtn6QVaT7Tejf+86dO0VMg7X1g1zLZKk9ePAAarVa/G01atQI1nzfWl1CrlnC+kd3y4mEhAQxHubv72/UTusU0EDQ4PlXX30lQrfpj++DDz6wyOhGKX0h6I+R3IF0c6BxNJqu0a5dO1hTPw4cOCBcOjQtQDc+snTpUhFWb039oAjAMWPG6AOA3n77bYvqg7l/W5aOlH7QgwxNyyJoXxJ/EiBrvG+Ra7lz587ib6tXr154+umnYa1/W8nJyWKcc926dSXyuSyapcwzzzwjXrbA9u3bYe107NhRPMBYO+TFOHXqFGwN8gJYK7Vr1xYPlbYARTHbSiSzl5dXiY73s3u2mFCkIoX95/0xaJ2mMlgTttIX7oflYSt94X5YHj7l1BcWzWLi5OQkJsbv2LFD30YWDK1bksvSnvrC/bA8bKUv3A/Lw6mc+sLu2UKgwXzKUKKDQvvJJUaTfWvUqCFCnUeMGCHSmZG77JtvvhHjfSNHjoSlYSt94X5YVj9sqS/cD8vqh8X2pURicG0Uml5BX1He14gRI/T7fP/995oaNWponJycRPjz4cOHNZaIrfSF+2F52EpfuB+Wxy4L7AvnnmUYhmEYifCYJsMwDMNIhEWTYRiGYSTCoskwDMMwEmHRZBiGYRiJsGgyDMMwjERYNBmGYRhGIiyaDMMwDCMRFk2GYRiGkQiLJsMwDMNIhEWTYcoBKrgsk8ksqrwX1SBs27YtXFxc0KJFC1gK9D3pap8WxSeffGJR187YHiyajF1CdRvpZjx37lyjdro5U7s9Mn36dFSoUAGXLl0yqhxRVhQkeDExMTZT25Gxflg0GbuFLKp58+bhwYMHsBWysrKKfey1a9dEke6goCBUrlwZZQWlv1YqlQVup9qIzs7OZXY9DFMYLJqM3dKzZ09xQ54zZ45Z1g+VH6pZs6aR1dq/f3989tln8Pf3R8WKFfHpp58KIXj//fdFGaPAwEAsWrTIpEu0ffv2QsCbNGmCPXv2GG0/e/assLLc3d3FuYcNG4aEhAT99q5du+Ktt97ChAkTRFHe3r17m+wH1Rmka6LrIAGiPoWHh+u3k3V94sQJsQ8tU79Nofs8enl5eYnPnDp1qhA+HUuXLhWlmjw8PMT3+/LLLyMuLk6/fffu3eIztm7dKuoh0vUsW7YMM2bMQGRkpNhGr8WLF5t0z96+fRsvvfSS+F7JMqbPOnLkSAG/IPDbb7+hUaNG4jtu2LAhfvrpJ6OHDOpLQECA2E4PDIX9PTAMiyZjt1DVdxK677//XtyIH4edO3fi7t272Lt3L+bPny9cnU8//TS8vb3FDf3111/H2LFj830Oieq7776LkydPisK5/fr1w/3798W2pKQkdO/eHS1btsTx48eFyFFV+kGDBhmd448//hAFeQ8cOICff/7Z5PV9++23+Oqrr/Dll1/i9OnTQlyfeeYZXLlyRe8Cbdy4sbgWWn7vvfcK7Ct9noODA44ePSrOS/0lYdKRnZ2NmTNnCgEksaPxW3qwyMuUKVOEe/zChQt44oknxGfTNdDn02vw4MEm6yt26dIFd+7cwaZNm8RnfPDBB+KhwBTLly/HtGnTMHv2bPE59HuTyFMfiO+++06cZ/Xq1cItTfsbPhAxTD5KtfAYw1goVI/v2WefFctt27bVjBo1SiyvX79e1OvTMX36dE3z5s2Njv366681QUFBRueidZVKpW9r0KCBplOnTvp1pVKpqVChgmblypVi/caNG+Jz5s6dq98nOztbExgYqJk3b55YnzlzpqZXr15Gn33r1i1x3KVLl8R6ly5dNC1btiyyv1WrVtXMnj3bqC00NFTzxhtv6Nepn9TfwqDPa9SokUatVuvbJk+eLNoK4tixY+KaHz58aFQjccOGDUb7mfquCdqXfhfil19+0Xh4eGju379v8rPynqNOnTqaFStWGO1D32u7du3E8ttvv63p3r27UX8YpjDY0mTsHhrXJMuDLJHiQhaSXJ77z4lcqU2bNjWyammc0NBNSZB1qYOsN3I16q6DrKhdu3YJ16zuRe5F3fijDnJxFkZKSoqwgjt06GDUTuvF6TNF2BoGS1EfyGJVqVRindy8ZDHXqFFDuGjJMiSio6ONzkN9NReKNibLm1yzRZGamiq+p1dffdXoO5w1a5b++yMLmM7ZoEEDjB8/Htu2bTP7mhj7wqG8L4BhypvOnTsLd+WHH36Yz41IQpi3Tju5H/Pi6OhotE6iYqqtIDeiKcgVSeJDop4XGoPTQeN6lgIJFX2X9CJXp6+vrxBLWs8bpFSc63Z1dTXr+yMWLlyINm3aGG2jhxgiJCQEN27cEOOr27dvF65vGuteu3at2dfG2AcsmgwDiLE1Co4hi8MQuunHxsYK4dRZVyU5t/Lw4cNCtAkKHCIrjQJTdDf0devWiTE2skKLi6enJ6pWrSrGPHVWH0HrYWFhZp8vb9AN9aFevXpCiCiwicZk6fusXr262E7jsVKgcVmdtVoQzZo1E+OniYmJRVqbZO1Tv69fv44hQ4YU+v3Q+Cm9Bg4ciD59+kg6P2OfsHuWYQDhSqUbKwWG5I0WjY+Px+effy5cej/++KOwSkoKOt/69euF2Lz55pti+suoUaPENlqnmzdFih47dkx8/r///ouRI0cWKS55oYAjslhXrVolAl4oCIfE/5133jH7mslynDRpkjjPypUrRSCV7jzkkiXxozYSKwqyoaAgKdDDAVl9dF0UIZyZmZlvH/ouKCKXopVJ9Okz6MHi0KFDJs9JEbkUDUu/6+XLl3HmzBkRxUzBSwS9Ux/o+6fta9asEeenCGiGMQWLJsPkQNMt8rpPaaoCTVEgcWvevLmIGC0sstRcyCKjF517//79QmRoGgehsw5JIHv16iWEnaaW0A3dcPxUCjReR0JHEap0HorEpc8iC9Fchg8fjvT0dGGlkrCTYI4ZM0ZvmdNUERKf4OBg0TeK2JXCgAEDhJXXrVs3cR4Ss7yQINO4o5+fH/r27Sv6Qp+hc7fm5bXXXhOWKQkl7UuWNl1frVq1xHYac6UHIhpfDQ0NFZG+W7ZsMfv7ZewHGUUDlfdFMAxjHZDlTW5smqvKMPYIP04xDMMwjERYNBmGYRhGIuyeZRiGYRiJsKXJMAzDMBJh0WQYhmEYibBoMgzDMIxEWDQZhmEYRiIsmgzDMAwjERZNhmEYhpEIiybDMAzDSIRFk2EYhmEgjf8H9GXMxW8QJV4AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(5, 3))\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu, marker='o', label='2 GPUs')\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_4gpu, marker='o', label='4 GPUs')\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_6gpu, marker='o', label='6 GPUs')\n", + "plt.xlabel('Number of particles')\n", + "plt.ylabel('Time in seconds on RTX 2080ti')\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(5, 3))\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu, marker='o', label='2 GPUs')\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_4gpu, marker='o', label='4 GPUs')\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_6gpu, marker='o', label='6 GPUs')\n", + "plt.plot(particle_number, time_on_mac_2cpu, marker='x', label='MacBook Pro 2 CPUs')\n", + "plt.xlabel('Number of particles')\n", + "plt.ylabel('Time in seconds')\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(5, 3))\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu/particle_number_gpu, marker='o', label='2 GPUs')\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_4gpu/particle_number_gpu, marker='o', label='4 GPUs')\n", + "plt.plot(particle_number_gpu, time_on_compgpu4_6gpu/particle_number_gpu, marker='o', label='6 GPUs')\n", + "plt.xlabel('Number of particles')\n", + "plt.ylabel('Time per particle in seconds')\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Time in seconds on M1')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(10, 6))\n", + "plt.figure(figsize=(5, 3))\n", "plt.plot(particle_number, time_on_mac_2cpu, marker='o', linestyle='-')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", - "plt.xlabel('Number of Particles')\n", - "plt.ylabel('Time (seconds)')\n", - "plt.title('Scaling of Rubix Pipeline with Number of Particles')" + "plt.xlabel('Number of particles')\n", + "plt.ylabel('Time in seconds on M1')\n", + "#plt.title('Scaling of Rubix Pipeline with Number of Particles')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Time per particle in seconds')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(10, 6))\n", + "plt.figure(figsize=(5, 3))\n", "plt.plot(particle_number, time_on_mac_2cpu/particle_number, marker='o', linestyle='-')\n", "plt.xscale('log')\n", "plt.yscale('log')\n", - "plt.xlabel('Number of Particles')\n", - "plt.ylabel('Time (seconds) per Particle')\n", - "plt.title('Scaling of Rubix Pipeline with Number of Particles')" + "plt.xlabel('Number of particles')\n", + "plt.ylabel('Time per particle in seconds')\n", + "#plt.title('Scaling of Rubix Pipeline with Number of Particles')" ] } ], @@ -280,7 +578,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.8" } }, "nbformat": 4, From 760b063a61be8e1c0af488f33d6e7341a1b369ba Mon Sep 17 00:00:00 2001 From: anschaible Date: Wed, 2 Jul 2025 16:56:46 +0200 Subject: [PATCH 57/76] minor: deleting some lines that were already commented out in the fits file saving process --- rubix/core/fits.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/rubix/core/fits.py b/rubix/core/fits.py index 300bae58..edf76847 100644 --- a/rubix/core/fits.py +++ b/rubix/core/fits.py @@ -25,17 +25,6 @@ def store_fits(config, data, filepath): logger_config = config.get("logger", None) logger = get_logger(logger_config) - """ - if "cube_type" not in config["data"]["args"]: - datacube = data.stars.datacube - parttype = "stars" - elif config["data"]["args"]["cube_type"] == "stars": - datacube = data.stars.datacube - parttype = "stars" - elif config["data"]["args"]["cube_type"] == "gas": - datacube = data.gas.datacube - parttype = "gas" - """ datacube = data telescope = get_telescope(config) From 5235b6f7ac697efead4409790d81dc2c2f2e5d92 Mon Sep 17 00:00:00 2001 From: anschaible Date: Thu, 3 Jul 2025 11:30:56 +0200 Subject: [PATCH 58/76] change timing --- ...bix_pipeline_single_function_scaling.ipynb | 170 +++++++++++++----- rubix/core/pipeline.py | 15 +- 2 files changed, 136 insertions(+), 49 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index c4bf8893..d97727b1 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -21,7 +21,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)]\n" + "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)]\n" ] } ], @@ -33,7 +33,7 @@ "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '1,3,5,6,7,8'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '1,3,4,5'\n", "\n", "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", @@ -84,23 +84,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-07-02 15:36:42,036 - rubix - INFO - \n", + "2025-07-03 11:27:06,244 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-07-02 15:36:42,036 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", - "2025-07-02 15:36:42,037 - rubix - INFO - JAX version: 0.6.0\n", - "2025-07-02 15:36:42,037 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5)] devices\n" + "2025-07-03 11:27:06,245 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", + "2025-07-03 11:27:06,246 - rubix - INFO - JAX version: 0.6.0\n", + "2025-07-03 11:27:06,246 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" ] } ], @@ -135,7 +135,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 500000,\n", + " \"subset_size\": 100000,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -205,29 +205,93 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-07-02 15:36:42,879 - rubix - INFO - Getting rubix data...\n", - "2025-07-02 15:36:42,880 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-07-02 15:36:42,966 - rubix - INFO - Centering stars particles\n", - "2025-07-02 15:36:45,038 - rubix - WARNING - The Subset value is set in config. Using only subset of size 500000 for stars\n", - "2025-07-02 15:36:45,039 - rubix - INFO - Data loaded with 500000 star particles and 0 gas particles.\n" + "2025-07-03 11:27:07,059 - rubix - INFO - Getting rubix data...\n", + "2025-07-03 11:27:07,061 - rubix - INFO - Loading data from IllustrisAPI\n", + "2025-07-03 11:27:07,062 - rubix - INFO - Reusing existing file galaxy-id-11.hdf5. If you want to download the data again, set reuse=False.\n", + "2025-07-03 11:27:07,101 - rubix - INFO - Loading data into input handler\n", + "2025-07-03 11:27:07,102 - rubix - DEBUG - Loading data from Illustris file..\n", + "2025-07-03 11:27:07,102 - rubix - DEBUG - Checking if the fields are present in the file...\n", + "2025-07-03 11:27:07,102 - rubix - DEBUG - Keys in the file: \n", + "2025-07-03 11:27:07,103 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", + "2025-07-03 11:27:07,103 - rubix - DEBUG - Matching fields: {'Header', 'SubhaloData', 'PartType4', 'PartType0'}\n", + "2025-07-03 11:27:07,107 - rubix - DEBUG - Found 102609 valid particles out of 102609\n", + "2025-07-03 11:27:07,277 - rubix - DEBUG - Found 643940 valid particles out of 643940\n", + "2025-07-03 11:27:07,714 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", + "2025-07-03 11:27:13,988 - rubix - DEBUG - Converting to Rubix format..\n", + "2025-07-03 11:27:13,989 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", + "2025-07-03 11:27:13,990 - rubix - DEBUG - Keys in the particle data: dict_keys(['gas', 'stars'])\n", + "2025-07-03 11:27:13,990 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", + "2025-07-03 11:27:13,990 - rubix - DEBUG - Matching fields: {'gas', 'stars'}\n", + "2025-07-03 11:27:13,990 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", + "2025-07-03 11:27:13,991 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", + "2025-07-03 11:27:13,991 - rubix - DEBUG - Required fields for gas: ['coords', 'density', 'mass', 'metallicity', 'metals', 'sfr', 'internal_energy', 'velocity', 'electron_abundance']\n", + "2025-07-03 11:27:13,991 - rubix - DEBUG - Available fields in particle_data[gas]: ['coords', 'density', 'electron_abundance', 'metallicity', 'metals', 'internal_energy', 'mass', 'sfr', 'velocity']\n", + "2025-07-03 11:27:13,992 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-07-03 11:27:13,992 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", + "2025-07-03 11:27:14,000 - rubix - DEBUG - Converting redshift for galaxy data into \n", + "2025-07-03 11:27:14,001 - rubix - DEBUG - Converting center for galaxy data into kpc\n", + "2025-07-03 11:27:14,002 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", + "2025-07-03 11:27:14,002 - rubix - DEBUG - Converting coords for particle type gas into kpc\n", + "2025-07-03 11:27:14,006 - rubix - DEBUG - Converting density for particle type gas into Msun/kpc^3\n", + "2025-07-03 11:27:14,019 - rubix - DEBUG - Converting electron_abundance for particle type gas into \n", + "2025-07-03 11:27:14,021 - rubix - DEBUG - Converting metallicity for particle type gas into \n", + "2025-07-03 11:27:14,024 - rubix - DEBUG - Converting metals for particle type gas into \n", + "2025-07-03 11:27:14,029 - rubix - DEBUG - Converting internal_energy for particle type gas into erg/g\n", + "2025-07-03 11:27:14,035 - rubix - DEBUG - Converting mass for particle type gas into Msun\n", + "2025-07-03 11:27:14,046 - rubix - DEBUG - Converting sfr for particle type gas into Msun/yr\n", + "2025-07-03 11:27:14,051 - rubix - DEBUG - Converting velocity for particle type gas into km/s\n", + "2025-07-03 11:27:14,054 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", + "2025-07-03 11:27:14,073 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", + "2025-07-03 11:27:14,080 - rubix - DEBUG - Converting metallicity for particle type stars into \n", + "2025-07-03 11:27:14,083 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", + "2025-07-03 11:27:14,089 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", + "2025-07-03 11:27:14,182 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-07-03 11:27:14,293 - rubix - INFO - Centering stars particles\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted to Rubix format!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-03 11:27:16,311 - rubix - WARNING - The Subset value is set in config. Using only subset of size 500000 for stars\n", + "2025-07-03 11:27:16,312 - rubix - INFO - Data loaded with 500000 star particles and 0 gas particles.\n", + "2025-07-03 11:27:16,313 - rubix - INFO - Data preparation completed in 9.25 seconds.\n" ] }, { "data": { "text/plain": [ - "(10000000, 3)" + "(500000, 3)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] } ], "source": [ @@ -235,12 +299,13 @@ "import jax.numpy as jnp\n", "\n", "inputdata = pipe.prepare_data()\n", + "#inputdata = pipe.prepare_data()\n", "coords = inputdata.stars.coords\n", "vel = inputdata.stars.velocity\n", "mass = inputdata.stars.mass\n", "age = inputdata.stars.age\n", "met = inputdata.stars.metallicity\n", - "factor = 20\n", + "factor = 1\n", "inputdata.stars.coords = jnp.concatenate([coords]*factor, axis=0)\n", "inputdata.stars.velocity = jnp.concatenate([vel]*factor, axis=0)\n", "inputdata.stars.mass = jnp.concatenate([mass]*factor, axis=0)\n", @@ -251,52 +316,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-07-02 15:36:45,348 - rubix - INFO - Setting up the pipeline...\n", - "2025-07-02 15:36:45,350 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-07-02 15:36:45,351 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-07-02 15:36:45,353 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-07-03 11:27:16,329 - rubix - INFO - Setting up the pipeline...\n", + "2025-07-03 11:27:16,330 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-07-03 11:27:16,331 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-07-03 11:27:16,334 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-02 15:36:45,372 - rubix - INFO - Getting cosmology...\n", + "2025-07-03 11:27:16,354 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-02 15:36:45,782 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-07-03 11:27:16,806 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-02 15:36:45,800 - rubix - INFO - Getting cosmology...\n", - "2025-07-02 15:36:45,879 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-07-03 11:27:16,824 - rubix - INFO - Getting cosmology...\n", + "2025-07-03 11:27:16,900 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-02 15:36:46,165 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-07-03 11:27:17,050 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-02 15:36:46,183 - rubix - INFO - Getting cosmology...\n", + "2025-07-03 11:27:17,071 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-02 15:36:46,341 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-07-03 11:27:17,379 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-02 15:36:46,830 - rubix - INFO - Assembling the pipeline...\n", - "2025-07-02 15:36:46,830 - rubix - INFO - Compiling the expressions...\n", - "2025-07-02 15:36:46,831 - rubix - INFO - Number of devices: 6\n", - "2025-07-02 15:36:47,812 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-07-02 15:36:47,920 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-07-02 15:36:47,926 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-07-02 15:36:47,954 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-07-02 15:36:48,157 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-07-02 15:36:48,158 - rubix - INFO - Convolving with PSF...\n", - "2025-07-02 15:36:48,163 - rubix - INFO - Convolving with LSF...\n", - "2025-07-02 15:36:48,173 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-07-02 15:45:06,127 - rubix - INFO - Pipeline run completed in 500.78 seconds.\n" + "2025-07-03 11:27:17,852 - rubix - INFO - Assembling the pipeline...\n", + "2025-07-03 11:27:17,853 - rubix - INFO - Compiling the expressions...\n", + "2025-07-03 11:27:17,854 - rubix - INFO - Number of devices: 4\n", + "2025-07-03 11:27:18,478 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-07-03 11:27:18,585 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-07-03 11:27:18,590 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-07-03 11:27:18,617 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-07-03 11:27:18,786 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-07-03 11:27:18,788 - rubix - INFO - Convolving with PSF...\n", + "2025-07-03 11:27:18,791 - rubix - INFO - Convolving with LSF...\n", + "2025-07-03 11:27:18,797 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "E0703 11:27:34.751865 2117982 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: INTERNAL: jaxlib/gpu/solver_handle_pool.cc:37: operation gpusolverDnCreate(&handle) failed: cuSolver internal error\n" ] } ], @@ -308,7 +373,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rubixdata = pipe.run_sharded(inputdata)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -320,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -356,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -371,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -410,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -450,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -526,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -578,7 +652,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 69b961aa..a3f41af1 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -75,6 +75,7 @@ def prepare_data(self): Object containing particle data with attributes such as: 'coords', 'velocities', 'mass', 'age', and 'metallicity' under stars and gas. """ + t1 = time.time() self.logger.info("Getting rubix data...") rubixdata = get_rubix_data(self.user_config) star_count = ( @@ -84,6 +85,10 @@ def prepare_data(self): self.logger.info( f"Data loaded with {star_count} star particles and {gas_count} gas particles." ) + t2 = time.time() + self.logger.info( + "Data preparation completed in %.2f seconds.", t2 - t1 + ) return rubixdata @jaxtyped(typechecker=typechecker) @@ -318,11 +323,19 @@ def _shard_pipeline(sharded_rubixdata): check_rep=False, ) + time_mid = time.time() sharded_result = sharded_pipeline(inputdata) time_end = time.time() self.logger.info( - "Pipeline run completed in %.2f seconds.", time_end - time_start + "Sharding completed in %.2f seconds.", time_mid - time_start + ) + self.logger.info( + "Sharded pipeline run completed in %.2f seconds.", time_end - time_mid + ) + self.logger.info( + "Total time for sharded pipeline run: %.2f seconds.", + time_end - time_start, ) # final_cube = jnp.sum(partial_cubes, axis=0) From 315a2b6223a7b87a1022ae293b797024aed333a5 Mon Sep 17 00:00:00 2001 From: anschaible Date: Thu, 3 Jul 2025 17:12:11 +0200 Subject: [PATCH 59/76] scaling --- ...bix_pipeline_single_function_scaling.ipynb | 241 +++++++++--------- 1 file changed, 114 insertions(+), 127 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index d97727b1..aff63c30 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -21,7 +21,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)]\n" + "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5), CudaDevice(id=6), CudaDevice(id=7)]\n" ] } ], @@ -33,7 +33,7 @@ "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", "\n", "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '1,3,4,5'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '1,3,4,5,6,7,8,9'\n", "\n", "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", "\n", @@ -84,23 +84,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-07-03 11:27:06,244 - rubix - INFO - \n", + "2025-07-03 12:16:01,439 - rubix - INFO - \n", " ___ __ _____ _____ __\n", " / _ \\/ / / / _ )/ _/ |/_/\n", " / , _/ /_/ / _ |/ /_> <\n", "/_/|_|\\____/____/___/_/|_|\n", "\n", "\n", - "2025-07-03 11:27:06,245 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", - "2025-07-03 11:27:06,246 - rubix - INFO - JAX version: 0.6.0\n", - "2025-07-03 11:27:06,246 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3)] devices\n" + "2025-07-03 12:16:01,440 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", + "2025-07-03 12:16:01,440 - rubix - INFO - JAX version: 0.6.0\n", + "2025-07-03 12:16:01,441 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5), CudaDevice(id=6), CudaDevice(id=7)] devices\n" ] } ], @@ -135,7 +135,7 @@ " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 100000,\n", + " \"subset_size\": 1,\n", " },\n", " },\n", " \"simulation\": {\n", @@ -205,93 +205,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-07-03 11:27:07,059 - rubix - INFO - Getting rubix data...\n", - "2025-07-03 11:27:07,061 - rubix - INFO - Loading data from IllustrisAPI\n", - "2025-07-03 11:27:07,062 - rubix - INFO - Reusing existing file galaxy-id-11.hdf5. If you want to download the data again, set reuse=False.\n", - "2025-07-03 11:27:07,101 - rubix - INFO - Loading data into input handler\n", - "2025-07-03 11:27:07,102 - rubix - DEBUG - Loading data from Illustris file..\n", - "2025-07-03 11:27:07,102 - rubix - DEBUG - Checking if the fields are present in the file...\n", - "2025-07-03 11:27:07,102 - rubix - DEBUG - Keys in the file: \n", - "2025-07-03 11:27:07,103 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", - "2025-07-03 11:27:07,103 - rubix - DEBUG - Matching fields: {'Header', 'SubhaloData', 'PartType4', 'PartType0'}\n", - "2025-07-03 11:27:07,107 - rubix - DEBUG - Found 102609 valid particles out of 102609\n", - "2025-07-03 11:27:07,277 - rubix - DEBUG - Found 643940 valid particles out of 643940\n", - "2025-07-03 11:27:07,714 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", - "2025-07-03 11:27:13,988 - rubix - DEBUG - Converting to Rubix format..\n", - "2025-07-03 11:27:13,989 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", - "2025-07-03 11:27:13,990 - rubix - DEBUG - Keys in the particle data: dict_keys(['gas', 'stars'])\n", - "2025-07-03 11:27:13,990 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", - "2025-07-03 11:27:13,990 - rubix - DEBUG - Matching fields: {'gas', 'stars'}\n", - "2025-07-03 11:27:13,990 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", - "2025-07-03 11:27:13,991 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", - "2025-07-03 11:27:13,991 - rubix - DEBUG - Required fields for gas: ['coords', 'density', 'mass', 'metallicity', 'metals', 'sfr', 'internal_energy', 'velocity', 'electron_abundance']\n", - "2025-07-03 11:27:13,991 - rubix - DEBUG - Available fields in particle_data[gas]: ['coords', 'density', 'electron_abundance', 'metallicity', 'metals', 'internal_energy', 'mass', 'sfr', 'velocity']\n", - "2025-07-03 11:27:13,992 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-07-03 11:27:13,992 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", - "2025-07-03 11:27:14,000 - rubix - DEBUG - Converting redshift for galaxy data into \n", - "2025-07-03 11:27:14,001 - rubix - DEBUG - Converting center for galaxy data into kpc\n", - "2025-07-03 11:27:14,002 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", - "2025-07-03 11:27:14,002 - rubix - DEBUG - Converting coords for particle type gas into kpc\n", - "2025-07-03 11:27:14,006 - rubix - DEBUG - Converting density for particle type gas into Msun/kpc^3\n", - "2025-07-03 11:27:14,019 - rubix - DEBUG - Converting electron_abundance for particle type gas into \n", - "2025-07-03 11:27:14,021 - rubix - DEBUG - Converting metallicity for particle type gas into \n", - "2025-07-03 11:27:14,024 - rubix - DEBUG - Converting metals for particle type gas into \n", - "2025-07-03 11:27:14,029 - rubix - DEBUG - Converting internal_energy for particle type gas into erg/g\n", - "2025-07-03 11:27:14,035 - rubix - DEBUG - Converting mass for particle type gas into Msun\n", - "2025-07-03 11:27:14,046 - rubix - DEBUG - Converting sfr for particle type gas into Msun/yr\n", - "2025-07-03 11:27:14,051 - rubix - DEBUG - Converting velocity for particle type gas into km/s\n", - "2025-07-03 11:27:14,054 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", - "2025-07-03 11:27:14,073 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", - "2025-07-03 11:27:14,080 - rubix - DEBUG - Converting metallicity for particle type stars into \n", - "2025-07-03 11:27:14,083 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", - "2025-07-03 11:27:14,089 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", - "2025-07-03 11:27:14,182 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-07-03 11:27:14,293 - rubix - INFO - Centering stars particles\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Converted to Rubix format!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-03 11:27:16,311 - rubix - WARNING - The Subset value is set in config. Using only subset of size 500000 for stars\n", - "2025-07-03 11:27:16,312 - rubix - INFO - Data loaded with 500000 star particles and 0 gas particles.\n", - "2025-07-03 11:27:16,313 - rubix - INFO - Data preparation completed in 9.25 seconds.\n" + "2025-07-03 12:16:02,261 - rubix - INFO - Getting rubix data...\n", + "2025-07-03 12:16:02,263 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", + "2025-07-03 12:16:02,338 - rubix - INFO - Centering stars particles\n", + "2025-07-03 12:16:04,253 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1 for stars\n", + "2025-07-03 12:16:04,254 - rubix - INFO - Data loaded with 1 star particles and 0 gas particles.\n", + "2025-07-03 12:16:04,255 - rubix - INFO - Data preparation completed in 1.99 seconds.\n" ] }, { "data": { "text/plain": [ - "(500000, 3)" + "(1, 3)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." - ] } ], "source": [ @@ -316,52 +253,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-07-03 11:27:16,329 - rubix - INFO - Setting up the pipeline...\n", - "2025-07-03 11:27:16,330 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-07-03 11:27:16,331 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-07-03 11:27:16,334 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-07-03 12:16:04,269 - rubix - INFO - Setting up the pipeline...\n", + "2025-07-03 12:16:04,270 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-07-03 12:16:04,271 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-07-03 12:16:04,274 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-03 11:27:16,354 - rubix - INFO - Getting cosmology...\n", + "2025-07-03 12:16:04,293 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-03 11:27:16,806 - rubix - INFO - Calculating spatial bin edges...\n", + "2025-07-03 12:16:04,700 - rubix - INFO - Calculating spatial bin edges...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-03 11:27:16,824 - rubix - INFO - Getting cosmology...\n", - "2025-07-03 11:27:16,900 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-07-03 12:16:04,715 - rubix - INFO - Getting cosmology...\n", + "2025-07-03 12:16:04,784 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-03 11:27:17,050 - rubix - DEBUG - SSP Wave: (5994,)\n", + "2025-07-03 12:16:04,948 - rubix - DEBUG - SSP Wave: (5994,)\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-03 11:27:17,071 - rubix - INFO - Getting cosmology...\n", + "2025-07-03 12:16:05,169 - rubix - INFO - Getting cosmology...\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-03 11:27:17,379 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "2025-07-03 12:16:05,327 - rubix - DEBUG - Method not defined, using default method: cubic\n", "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", " warnings.warn(\n", - "2025-07-03 11:27:17,852 - rubix - INFO - Assembling the pipeline...\n", - "2025-07-03 11:27:17,853 - rubix - INFO - Compiling the expressions...\n", - "2025-07-03 11:27:17,854 - rubix - INFO - Number of devices: 4\n", - "2025-07-03 11:27:18,478 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-07-03 11:27:18,585 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-07-03 11:27:18,590 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-07-03 11:27:18,617 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-07-03 11:27:18,786 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-07-03 11:27:18,788 - rubix - INFO - Convolving with PSF...\n", - "2025-07-03 11:27:18,791 - rubix - INFO - Convolving with LSF...\n", - "2025-07-03 11:27:18,797 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "E0703 11:27:34.751865 2117982 pjrt_stream_executor_client.cc:2839] Execution of replica 0 failed: INTERNAL: jaxlib/gpu/solver_handle_pool.cc:37: operation gpusolverDnCreate(&handle) failed: cuSolver internal error\n" + "2025-07-03 12:16:05,811 - rubix - INFO - Assembling the pipeline...\n", + "2025-07-03 12:16:05,812 - rubix - INFO - Compiling the expressions...\n", + "2025-07-03 12:16:05,813 - rubix - INFO - Number of devices: 8\n", + "2025-07-03 12:16:06,991 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-07-03 12:16:07,107 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-07-03 12:16:07,112 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-07-03 12:16:07,128 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-07-03 12:16:07,279 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-07-03 12:16:07,279 - rubix - INFO - Convolving with PSF...\n", + "2025-07-03 12:16:07,283 - rubix - INFO - Convolving with LSF...\n", + "2025-07-03 12:16:07,289 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-07-03 12:16:24,983 - rubix - INFO - Sharding completed in 2.57 seconds.\n", + "2025-07-03 12:16:24,987 - rubix - INFO - Sharded pipeline run completed in 18.15 seconds.\n", + "2025-07-03 12:16:24,988 - rubix - INFO - Total time for sharded pipeline run: 20.71 seconds.\n" ] } ], @@ -373,16 +312,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-07-03 12:16:25,002 - rubix - INFO - Setting up the pipeline...\n", + "2025-07-03 12:16:25,005 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-07-03 12:16:25,006 - rubix - DEBUG - Roataion Type found: edge-on\n", + "2025-07-03 12:16:25,009 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-03 12:16:25,033 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-03 12:16:25,054 - rubix - INFO - Calculating spatial bin edges...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-03 12:16:25,072 - rubix - INFO - Getting cosmology...\n", + "2025-07-03 12:16:25,164 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-03 12:16:25,241 - rubix - DEBUG - SSP Wave: (5994,)\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-03 12:16:25,262 - rubix - INFO - Getting cosmology...\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-03 12:16:25,355 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-07-03 12:16:25,437 - rubix - INFO - Assembling the pipeline...\n", + "2025-07-03 12:16:25,437 - rubix - INFO - Compiling the expressions...\n", + "2025-07-03 12:16:25,439 - rubix - INFO - Number of devices: 8\n", + "2025-07-03 12:16:25,622 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-07-03 12:16:25,718 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-07-03 12:16:25,722 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-07-03 12:16:25,724 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-07-03 12:16:25,735 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-07-03 12:16:25,736 - rubix - INFO - Convolving with PSF...\n", + "2025-07-03 12:16:25,740 - rubix - INFO - Convolving with LSF...\n", + "2025-07-03 12:16:25,745 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-07-03 12:16:40,686 - rubix - INFO - Sharding completed in 0.44 seconds.\n", + "2025-07-03 12:16:40,687 - rubix - INFO - Sharded pipeline run completed in 15.24 seconds.\n", + "2025-07-03 12:16:40,688 - rubix - INFO - Total time for sharded pipeline run: 15.68 seconds.\n" + ] + } + ], "source": [ "rubixdata = pipe.run_sharded(inputdata)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -394,22 +381,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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b5nnS+mMBXwbzsFSfVvyXgTl013LOs0mTJla9OXON3nzzTWVJ8o+A5ZIehfZL4TwroXCzaD07DzBNhTByl4LICGBHJZefNwY2LY0xi6IwedVxdKpRWFqWCYK9iPobWDYCuH3pwb7cBYHW44CK7W32tvTosaypBjuHZAWm+BkbAdp3YU6SlEmApjbl57Z5nhRMdh7nWptbZCcVWqTWCqfmquV8JruycP6UT0tcWCWf0DXLoCQKNp9o/v77b9VhgJWM2NKGMLWFIvnSSy+ppqnsAvDBBx+oazt6o242yC4cqm9ZNmPLaXsPRxDcVzjnvpxeOMntaP1+HrcRFEvjTALjFlvk5s2bqslz/vz5lTg2bdpUfc+ZQrE0vo6nZ8Zf7bQA2UaSmRPMkmB9VxpHxtNX/O7t0KGD+o5nlgXz8Y1FnlAH+P3M72SOjVNsmgHENma0QJmnb85tyxgXphyyrRi/p4sWLaqMoIzQqttxLBwTv++NO7bMnz9fTe2xTyinFFlC1jTl0e5FEihuf/31F77//nu1cDurLoIpU6aoOUd+wLQkteX333839IXjL4wCSXcG02Uo3osWLTJcg4FLdPlyTSuUwUT8ZTpDezS2LHu7pday7CRu3nW81BpBcDpYPScp3rIl4TawlNNN5qLe0/bRIuV5llzPyso9x48fR8GCBVGyZEkV06G1I9Po0qWL8qqxwTONCLYfY4OO2NjYdOcxTY/fncy9Z0uuR0FvIcWKMSQ8nyLdvXv3dO3SmI7IuuUsIN+6dWu0a9fuofGNHz9epRnyHLYtY9Ecwu9t5uRzai+juJPPPvtM/QxTDWlAmQZ+anBsfGigILMFGVMcOTXXtWtXdZzv06NHD9VOjd5RejHZz9PWVZQs7qpCFWefOAb08IlCSyfhL5GX4OAppszJdDZs3VUlM1JSdWj75UYcuXwHrzUqiXefsXEPOkFw9S4ZFLFPC9pnMO9dAnyDLDqVgkiRKleunBIAxo0wZ55WFj1xmzZtUgVgKJ7GXjRaa4wvoaVHdy3FggVsmII3bdo0lZGwfft2JbQZWZ59+vRRxWm0GBL29GT/S/7cE088YfbnmAXB6m6MUdEsTwoap90e1bGFlidFkNbunTt3lCX99ddfK6vaFNNr/Pe//8XGjRuVV1HjwoULKkuC98/PkFOJ/LlHNQDPzq4qFluegwYNUk8V7JzCN6fyc+H2kiVL1DGeI1gHO9S/07qc2p655Qyib+ld1oIguDZ0Q9Ky5BQU3ab8HqXAMBiT0D1LYaAbku5KbeEXv5ZtQOGlUUPxYKU3ZkRwPWnSpEe6i+mK1aBnj65cWm6E7zts2DAlqNzP9+UxU8uzdu3aVt83r0OhpwVtCfwcGExq/BlogVX8HGj58lp02/Lz/OGHH1Rjb1tj8ez0H3/8oYSTvxhj6C6lW5W/NBZQ4MAF62hSLhx1iodix5kbKnjos+elZZkgZBmfQL0FaAlntwC/dH70eT3nA8XqW/beWYQixXQ/LZOAAkZXLC1Lc+dmBC1HWq2PA4WTgZd0y9LS5Vxi586dHwoKCgqyzMo2hteyBn4OdBmba0rCz4caxLGycA8rzn311Vd4//33lRVtSRBqVrHY8uQEL+cgM4LHeI5gPXSDjzS0LDuPE1ekZZkgZBl2X6Dr1JKlVFN9VC08MroYkLuQ/jxLrsf3ziIUCVpSWiYB3a6MJ6GVSAEzXkwDi0wzErRrZARLq3L+UIPuT1q9tDQJ50Hpan3uueeURccgJLpFH4VvmkaYNhAxhkFKFFDOp1oCPwdWi6Ob2PRz0MSb36EssEPXN929HIexO9mu4kmrkj52DswU7hswYIB6OhCyRq1iYWheIQKs1seWZYIg5ACeXvp0FIWp8KW9bv2Z/rxshtYdm2NQlGg1UahoRTF+hDBilEGQjFKlRaWdR6tKEz6WTGXQJq1VzpWyStuaNWtUtkFm+Pj4qDRBWmcMRKJQssmHNt9JgWOwD4WYblNWerPEOAoPD1fCqAX1aCVXjeE8I+ugc96WAUt8YOD86/Tp081ek/fC2Bp+Ljt27FDnc/6T87YUad4DUyX5mdCtzHFfvXrV8CBgd/Hk5C6joehbpw+eA+PCbfq9+aHxHCHrDG+lb1m29OBl7JWWZYKQMzCPs+ssILeJtUaLlPttlOfJoBcKAuctGTnK71KKCINpNGuK86BMzaNQ0KXLiFh2uNIiU+lGZRYCrcPGjRsroWOk66PmExnYSQGjKNJi4zyiluVAJk6ciNDQUDVNR6OIc7IZBSAZQyv5yy+/VMGjjCJmuos5GGXLcbP7FXWkW7duKjDKHLwOLWEKJacIea98SKDrmik5DOxhzQFGB/MzYqrihAkT1JyyQ0TbGk/28hespafQnOfTkbWVMRwJe0bbmvL23H34Y/cF1CuZF3NefVL9BxIEIWvRk1bBikKcA42LAXJF6Oc4bWBx2htG21J86KZ1RxKyKdrW6nIWmsUp2IYhLcpg0b5L2HqKLcuuoVFZ/VOoIAg2hkJZoqG9RyE4CZ5ZcTVwYtsUNrKm6Sw8HoVDA/FiXX2u0rhlR6RlmSAIgjOLJ5N4OZnMJFT6mlnFx1hEOaGblRJ9wsO80aSUqn176NJt/HNAWpYJgpB9aAULhBwSz5EjR6rJWUY2MZKKJZUolsbJqLYuh+Qu5GXLsob6lmWMvJWWZYIgCE4qnozgYhQVI2sZQs3oJ+YSseagVmdRgluyj1calkC+XPqWZb/vkJZlgiAITimejD5i6LIGay0yn4aJq7RAMwozFrJGkJ833mxaRm1PXn0cd5Pu23tIguDQiOdLyMm/E4vFk1X/9+/f/1BOz7x589QxFlEQspceTxRFkbAAXGXLss2Pru4hCO4IE/61TiGC8Ci0vxPt7yarWJyqwoTTqVOnqpZg5gSU+xmJK2Qfvt6eeLtFOQz+fS++W3cSLzxRFKFBGZdIFAR3hFV5GMSoeb9YAECmkARzFieFk38n/Hvh302OFElgLUS+cUaJozzOdjqWtIRxNBypSIIpTFV5Jq1lWf9GJfGetCwThIfg1xgLt0gUqfAoKJws7mPuAcsaLbC6wpAr4sjiSdYeuYI+M3coS3TdsKdRMMS6rgSC4C6whBtzzgXBHHTVZmZx2rTCkJDzPF0uP54oEYZ/T8eqlmXjOkvLMkEwB78YH9cdJwg2qTAk5Dx0L4xora8dPG8XW5bdsfeQBEEQ3BoRTyehVrFQtKiob1k2fvkxew9HEATBrRHxdLKWZZ4ewLJDl7Hn3IPKToIgCIKDiif7rzGiNiPYhLRFixbZNS7BDGUjgtGpZmFD0XiJ9RIEQXBw8fzpp59Qp04d1a3cFDY+rVy5ssr5FGzLkBZlVdTttlOx2HD8mr2HIwiC4JZYLJ4UTXbwZm3bsWPHIjU1VVmbrHP7zjvvYPz48Vi6dKltRyugUEgAXtZali2VlmWCIAgOLZ7MeZk1axZ+//13TJ48GTVr1lRiykhQlu3r37+/bUcqGPhPk9II9vNGVPRtLJaWZYIgCI4fMFS3bl0lmhRMWp8ffPCBU1YVcmbCgnxVtSGtZVnSfWlZJgiC4LDi+euvv6JixYpKNA8fPowBAwagZcuWGDJkCBISEmw3SuEh+jZgyzI/nFUty87ZeziCIAhuhcXiycLvr776KkaPHo3Vq1ejXLly+Pzzz7F27VosWbIE1apVw9atW207WiFdy7K3mpVW25NXn5CWZYIgCI4oniy6vGfPHrz55pvp9tevXx979+5F69at0bhxY1uMUciA7nWKomhYIK7FJeLHTaftPRxBEAS3wWLx3LRpE8qU0TdnNiUgIEAFEa1atSo7xyZY0rKsZVm1/f36U7gRn2TvIQmCILgFFosnczi1fnkZ0ahRo+wYk2AF7aoWRIUCuXEn8T6+XXfC3sMRBEFwCywWT6lm45h4enrgndbl1PZPW8/i4s179h6SIAiCyyO1bV2Ap8vmx5MlwlTKyuRVUjReEATB1lhVT2/atGnIlStXpue89dZbjzsmISsty9qUR6dvt2D+rgt4tWFJlIkItvewBEEQXBYPnYX+WE9PTxQuXDjTRrP8Ej916hScDWu6hzsy/WftxIqoGLSsGIGpL9e293AEQRBcVgusctvu3LkTp0+fznCxVjhZI5fF5oODgxEeHo6OHTvi6NGj6c5h8YU33ngDefPmVVYv801jYmLSncMau23btkVgYKC6zvDhwzPtAOOqcO6TLcsooLulZZkgCILNsFg8aVVmN+vXr1fCuG3bNqxcuRLJycmqYlF8fLzhHFYvWrRoEebNm6fOv3TpEjp16mQ4npKSooQzKSkJW7ZsUd1fZs6ciY8++gjuRunwYHSuldaybKm0LBMEQbAZOgvx8PDQxcTE6GzJlStX+G2vW79+vXp98+ZNnY+Pj27evHmGcw4fPqzO2bp1q3q9ZMkSnaenp+7y5cuGc6ZMmaLLnTu3LjEx0aL3vXXrlrom187OxRt3dWXeX6IrNmKxbu0R2/6+BEEQXAlrtMBiy3PUqFGZBgv9+eefqFq16mMJOf3MJCwsTK137dqlrFG2PdMoX748ihYtaigFyDUL1UdERBjOadWqlfJdHzp0yOz7JCYmquPGi6tQMCQAveqltSxbdlRalgmCINgAq8Tz559/RufOnfHCCy9g+/btav+aNWtQo0YNvPTSS3jqqaeyPBAWmx88eLC6BhtrayUBfX19ERISku5cCiWPaecYC6d2XDuW0VwrJ4W1pUiRInAl/vO0vmXZ4ejbWLT/kr2HIwiC4L7i+dlnn6m6tmfOnMHff/+Npk2b4tNPP0XPnj3RrVs3XLhwAVOmTMnyQDj3yYbbv/32G2zNu+++q6xcbTl//jxcidAgX7zWWGtZdkxalgmCINhLPGfMmIEffvhBRdwuXboU9+7dUwE6J06cwMiRIxEaGprlQQwcOBCLFy9WHVqYDqMRGRmpAoFu3ryZ7nxG2/KYdo5p9K32WjvHFD8/PxWGbLy4asuyc7F38Zu0LBMEQbCPeDIdhNYmadiwIXx8fDBmzBgEBQVl+c0ZDUrhXLBggXL/lihRIt3xWrVqqfdhCzQNprJwLPXq1VOvuT5w4EC6uruM3KUgsveouxLo641BaS3Lvlx9HPGJ7pe6IwiCYHfxZJCNv7+/4TXnIrXAnsdx1c6ePRtz5sxRuZ6co+RCq5ZwPrJfv34YOnSoskoZQNSnTx8lmHXr1lXnMLWFIsk513379mH58uX44IMP1LVpYboz3Z8oimJ52bIsSVqWCYIg2KvCUP/+/VUhAvLNN9/gxRdfVAJnzMSJE/G4uaN0Effu3dtQJOHtt9/Gr7/+qgSckbTffvttOpfs2bNnMWDAAKxbt05Zwr169VJztOwE404Vhszx975LeOvXPcjl540N7zRBWJCvvYckCILgkFijBRaL59NPP/3IQgk8Tvers+HK4slUlXZfb8KhS7fxSoMS+OBZ93VlC4Ig5Lh4ujKuLJ5k/bGr6PXjv/D18sTa4U+jUEiAvYckCILgPrVtBeekUZl8qFsyDEkpqZi0UlqWCYIgPC4inu7Ssqx1ebX95+4LOBZzx95DEgRBcGpEPN2EGkVD0bpSJFit7/+Wp+9cIwiCIFiHiKcbMaxVWdWybGVUDHadjbX3cARBEJwWEU83a1nWpZa+ju+4pUelZZkgCEIWsSwR0gSWy/v3339VVR8WdDfm5ZdfzupYhBxgcIsyWLD3Iv49E4t1R6+iSflwew9JEATB9cWTjalZDD4uLk6F8hrnfnJbxNOxKZAnAL3rF8fUDacwbtkRNC6bH5705QqCIAi2c9uy2k/fvn2VeNICvXHjhmGJjZV5NGfgP0+XQrC/N45cvqMqEAmCIAg2Fs+LFy/irbfeMpTpE5yPkEBfvN64lNqesPKotCwTBEGwtXiytizbkgnOTZ+niiN/sB/Ox97Dr/9KyzJBEASbznm2bdsWw4cPR1RUFKpUqaJahhnTvn17ay8p2K1lWRl8sPAgvlpzHJ1rFUaQX5bixwRBENwOq2vbsrtKhhfz8EBKSgqcDVevbZsRySmpaDFxPc5cv4uhLcrirWZl7D0kQRAE16xty9SUjBZnFE53xsfLE2+3LKe2GX17PS7R3kMSBEFwCqRIgpvTtkoBVC6UG3GJ9/HN2pP2Ho4gCILriuf69evRrl07lC5dWi2c59y4cWP2j06wOczxfKeVvmj87G1nceHGXXsPSRAEwfXEc/bs2WjevLlKVWHKCpeAgAA0a9YMc+bMsc0oBZvSsEw+1C+VN61l2XF7D0cQBMH1AoYqVKiA/v37Y8iQIen2T5w4ET/88AMOHz4MZ8NdA4aM2Xv+Jjp+sxksGLVsUCOUiwy295AEQRBcJ2Do1KlTymVrCl23p0+ftvZygoNQvUgI2lSOBB+lpGWZIAgCslc8ixQpgtWrVz+0f9WqVeqY4LwMa1UOXp4eWHU4BjvPSKlFQRCEjPDOSm1bznPu3bsX9evXV/s2b96MmTNnYvLkydZeTnAgSuXPhS61CuO3HedV0fi5r9VLV/hfEARByKJ4DhgwAJGRkZgwYQLmzp1rmAf9/fff0aFDB2svJzgYg5uXxYI9F7HjzA2sPXoFTctH2HtIgiAIzh8w5IpIwFB6xi49jO/Xn0L5yGD881ZD5coVBEFwdW7bMmBIcH0GNC6F3IaWZRftPRxBEASHQ8RTMN+y7Om0lmUrjiHxvpRdFARBMEbEUzBLn/olEB7shws37uHX7dKyTBAEwRgRT8EsAb5eGNRc32XlqzUnVO1bQRAEIZvEk51UmLZy48aNx72U4GB0rV0EJfIF4Xp8EqZtPGXv4QiCIDiveA4ePBjTp083CGfjxo1Rs2ZNVSBh3bp1thijYNeWZWXV9g/SskwQBCHr4jl//nxUq1ZNbS9atEiV5Dty5Iiqdfv+++9beznBwXmmcgFUKZQH8Ukp+HrtCXsPRxAEwTnF89q1a6pIAlmyZAm6dOmCsmXLom/fvjhw4IAtxijYuWXZiNb6lmW/bDuH87HSskwQBMFq8YyIiEBUVJRy2S5btgwtWrRQ++/evQsvLy9bjFGwMw3K5MNTpfUtyyauPIqtJ6/jr70X1Tol1e1rbAiC4IZYXZ6vT58+6Nq1KwoUKKDqnrK3J9m+fTvKl9dbKG5HagpwdgsQFwPkigCK1Qc8XetBgg2zO5zYjAV7LqlFo0Aef4xqVxGtKxew6/gEQRAcWjxHjx6NypUr4/z588pl6+fnp/bT6hw5ciTcjqi/gWUjgNsPBAW5CwKtxwEV28NViL51z+z+y7cSMGD2bkx5saYIqCAIbkOWUlU6d+6sAoQKFy5s2NerVy+rC8Nv2LBB9QYtWLCgsmIXLlyY7njv3r3VfuOldevW6c6JjY1Fz549VR3CkJAQ9OvXD3Fxccgx4Zz7cnrhJLej9ft53AWga3bMoiizxzSnLY+LC1cQBHfBIsvzyy+/tPiCbFdmKfHx8Spyl8FGnTp1MnsOxXLGjBmG15qlq0HhjI6OxsqVK5GcnKzcyv3798ecOXNgc1ctLU6DfBjDfR7AspFA+bZO78L993Qsom8lZHicd8vjPK9eqbw5OjZBEASHFc9Jkyale3316lUVIERLj9y8eROBgYEIDw+3SjzbtGmjlsygWGrRvaYcPnxYBS3t2LEDtWvXVvu++uorPPPMMxg/fryyaG0G5zhNLc506IDbF/XnlWgIZ+bKnYRsPU8QBMEt3LbM5dSW//3vf6hevboSLrpMuXCbhRI++eSTbB8gCy9QlMuVK6d6iV6/ft1wbOvWrUrANeEkDGDy9PRUAUwZkZiYqFrPGC9Ww+Cg7DzPgQkP9s/W8wRBENxuzvPDDz9U1h3FTIPbtE4/+OCDbB0cXbazZs3C6tWrMW7cOKxfv15ZqkyTIZcvX1bCaoy3tzfCwsLUsYwYO3as6tmmLayOZDWMqs3O8xyYJ0qEqajazLp6suXnzbtJOTgqQRAEJxJPzi/ev/9wkXAKWkxM9lpZ3bt3R/v27VGlShV07NgRixcvVi7axy0D+O6776pmp9rCyGGrYToKo2ozk5TchfTnOTlshs10FGRyt4wVGvDLbgz+bY+IqCAILo/V4tmsWTO89tpr2L17t2Hfrl27lEtVy/m0FSVLlkS+fPlw4oS+TBznQq9cuZLuHAo7XckZzZNq86iMzjVerIZBQExHyUxSSjVz+mAhDaahMB0lMk961ywt0q96VMeAp0sp63Ph3ktoMWkDVh92fne1IAhCtuV5/vjjjyothfOMPj4+BsFq1aoVpk2bBlty4cIFNefJAg2kXr16KliJ4l2rVi21b82aNUhNTcWTTz4Jm8M8zq6zHs7z9MsNJN4G9vwMlG0JVGgHVxHQFhUjVVQtg4M4x0mXLi3TdtWAlhUjMGzePpy8Go9+P+3E8zUL46N2FZEnQP93IgiC4Cp46HS6LCXnHTt2TBWEJ6wsxPq21sJ8TM2KrFGjBiZOnIgmTZqoOUsuY8aMwfPPP6+syJMnT+Kdd97BnTt3VA1dLWWFc6B0F3/33XeGVBUKuzWpKgwY4twnXbhZskJNKwwVrQcseRvYNRPw9gd6LQKKPAF3ICE5BRNXHsMPG0+Bf1kRuf3w2fNV0aRc+rlpQRAER8MaLciyeGYHnLukWJpCy3bKlClqnnPPnj3KumTaScuWLVVEL+vratBFO3DgQNXhhVG2FFvmpebKlSvnxNMcKfeB314Aji8HAsKAfiuBfKXhLuw6G4th8/bj9LV49bpb7SJ4/9kKyO0vVqggCG4ongwMmjlzpoqA5XwjXaTG0G3qbNhEPElSPDCzLXBpDxBSDHhlFZDLfSywe0kpGL/iKH7cfFpZoQXz+GNc56poWCa/vYcmCIKQs+JJK4/i2bZtW0Nx+MwKKri1eJK4K8D0FsCNM0DBGkDvfwDfILgTnCMdPn8fzl7XtzPr8URRvN+2AnL5WT3lLgiC4JziyWhX5l6yio+rYFPxJNdO6AX0XixQphXQfQ7g5V7CcTfpPj5fdhQzt5xRrwuFBODzzlXxVOl89h6aIAiC1VpgdaqKr68vSpd2n7m7bIFznS/8rg8e4hzoP0Oh/JhuRKCvN0a3r4RfX62LwqEBuHjzHnpO244PFx5EfOLDecOCIAiOjNXi+fbbb2Py5MmwY5yRc8Jo2+en63NCd/8EbBwPd4SF45cPboSX6hZTr3/edhatJ2/AtlMPyi4KgiA4Ola7bZ977jmsXbtWpZJUqlTJkOup8eeff8LZsLnb1ph/fwCWDNNvd5wCVH8B7srmE9fwzvz9ygolvesXxzutyykrVRAEwaXctizETgFt3Lixmv80rhHLRXgET7wKPDVIv/33m8BJ54tOzi4437lscEMVQEQ4H9pm8kbsOBNr76EJgiA4bp6nW1qehOk9f74KHJwP+AYDfZYABarCnVl/7CpG/rFf9QVlAHffp0pgeKty8PdxjfKGgiC4ueVp3NNz06ZNauG2YAWenkDHb4HiDYGkO8AvXYCbWShO70I0Lpsfy4c0QtfahVUs1fRNp/HM5I3YdfaGvYcmCILw+OIZHx+Pvn37qhzPRo0aqYXVf/r166caZAsW4u0HdJsN5K8AxF0GfukM3HNvoWD1oc87V8OM3nVUWb9T1+LR5bstGLvksCr7JwiC4LTiOXToUNVXk+XwWDaPy19//aX2MRJXsIKAEODF+UBwAeDqEeC3nsD9RLg7TcqHY8XgxuhUs5Bqdfb9hlNo++VG7D1/095DEwRByHqRhPnz5+Ppp59Ot58RuF27dnVKF26Oz3macvkg8GNrvQu3Uid9SgtduwJWRcXg3QUHcPVOomp59nrjUhjUvAz8vGUuVBAEJ5rzpGvWuDC7Rnh4uLhts0pkZaD7bMDTGzj0J7DqI3uPyGFoXjECK4c0QsfqBZUV+u26k2j31SYcuHDL3kMTBMGNsVo82UNz1KhRSEhIMOy7d++eah/GY0IWKfk00OEb/faWr4Bt39l7RA5DSKAvvuheA9+9WAv5cvniWEwcOn67GRNWHEXS/fSNCQRBEBzSbXvw4EHV+DoxMRHVqlVT+/bt2wd/f38sX75cFU5wNuzutjVm4wRg9cf6SkRstM2G24KB2PgkfPTXQSzeH61el48Mxvgu1VC5kOQYC4Lg4P086Z795ZdfDM2wK1SogJ49eyIgIADOiEOJJ38drH2780d9LdyX/wKK1rXvmByQJQei8cHCg0pMvT09MLBpabzRpDR8vGSuWBAEF2+G7Sg4lHhqjbR/fxE4thQICE1rpF3G3qNyOK7FJarC8ksPXlavKxXMrazQCgUc4HcoCILTYdOAobFjx+LHH398aD/3jRs3ztrLCeZgu7LO04FCtfS5n7M7AXdi7D0qhyNfLj9827MmvuxRAyGBPjh06Tbaf70JX685jvspMhcqCILtsFo8v//+e5QvX/6h/Zzr/O47CXLJNtgwu8fvQGgJ4OY5YE5XIDHO3qNyONiMvX21glgxpBFaVIxAcooO41ccw3PfbsGxmDv2Hp4gCC6K1eJ5+fJlVV3IlPz58yM6Wh/EIWQTufIDL/4BBOYFovcC83rrXbrCQ4QH+2PqS7XwRbfqyBPggwMXb+HZLzdhyrqTYoUKgmB/8SxSpAg2b9780H7uY5k+IZvJWwp4YS7gHQCcWAn8M8TtGmlbY4V2rFFIWaHNyocjKSUV45YdQefvtuLEFbHaBUGwo3i++uqrGDx4MGbMmIGzZ8+qhfOdQ4YMUccEG1C4NtD5R8DDE9g9C9jwf/YekUMTkdsf03rVVsFDwf7eqqzfM19uxNQNJ5HCSguCIAiPidXRtjx95MiR+PLLL5GUlKT2McdzxIgR+Ogj56yM43DRthmxY7o+jYV0+Bao0dPeI3J4om/dw8g/DqiWZ6Rm0RAlqiXz57L30ARBcMdUlbi4OBw+fFjldpYpUwZ+fn5wVpxGPMmqMcCmifpSfi/8DpRubu8ROTz8E5+38wI+XhyFuMT78PP2xDuty6NP/eLwZMFcQRAE5FA/TwYOxcbGolSpUko4JV00h2j2EVC1G5B6H5jbC4jeZ+8ROcVcaNc6RVS/0IZl8iHxfio+WRyF7lO34cy1eHsPTxAEJ8Rq8bx+/TqaNWuGsmXL4plnnjFE2LKfp7QkywE8PID2XwMlGgNJcWmNtM/Ze1ROQaGQAMzq+wQ+fa4Kgny98O+ZWLSZvBE/bTmDVJkLFQTBluLJwCAfHx+cO3cOgYGBhv3dunXDsmXLrL2ckBW8fYFuPwPhlYC4GGB2Z+BurL1H5TRW6AtPFsWywY1Qv1Re3EtOwai/D+GFadtwPla6AgmCYCPxXLFihaokVLhw4XT7Oe/JyFshh/DPA/ScB+QuBFw7qm+knfyg042QOUXCAjG735P4pEMlBPh4YdupWLT6YgN+3nbWYIUyMnfryev4a+9FtZZIXUEQNLxhJfHx8eksTg3Ofzpz0JBTkqcQ0HO+vpH2uS3AgteAzjOkkbaFMFjopXrF0bhsOIbN34d/T8eqWrnLDkbjmcoF8PXaE4i+9eCBpEAef4xqVxGtKz9cJEQQBPfC6m/Zhg0bYtasWencYKmpqfj888/RpEmT7B6f8CgiKqY10vYBohYCKz+094icjqJ5A/Hbq3WVMPr7eGLziet4f+HBdMJJLt9KwIDZu5W4CoLg3mSpnycDhmrWrIk1a9agffv2OHTokLI8WWWI0bfOhlOlqmTE/nnAn6/ot1uNBer9x94jckpYiajN5A2qRq45mNgSmccfm0Y0hZekuQiCS2HTVJXKlSvj2LFjaNCgATp06KDcuJ06dcKePXucUjhdhqpdgOZj9NvL3wMOLbT3iJySq3cSMxROwiO0SOniFQTBfbF6zpNQmd9///3sH43weDw1CLh1AdjxA/BnfyBXBFCsnr1H5VRcuWNZ0NUXq44hLrGkyhv19/Gy+bgEQXAsrLY8mY6yadMmw+tvvvkG1atXxwsvvIAbN25k9/gEa3NA24wDyrUFUhKBX7sDV4/Ze1RO153FErafjsWrs3aixscr8drPO/HHrgu4Ea8vVykIgutjtXgOHz5c+YXJgQMHMHToUFUs4fTp02pbsDOeXsDz04DCdYCEm8Ds56WRthU8USJMRdVmNJvJ/XmDfPFyvWKq6ALzRJcfisHb8/ah9v9WocfUbZix+TQu3JCcUUFwZawWT4pkxYoV1fYff/yBdu3a4dNPP1UW6NKlS6261oYNG9TPs5UZo3YXLkw/T8dYJhabZ/9Q1tBt3rw5jh8/nu4cBir17NlTTe6GhISoSkesu+vW+AbqG2mHlQJusZF2FyBRGkNbAoOAGHVLTAVUe/2/5yrj4w6VsWlEEyx+swHealYG5SOD9Xmhp65jzKIoNBi3Fm2/3IjJq47jcPRtKV8pCO4unr6+vrh7V/9UvWrVKrRs2VJth4WFGSxSS2GwUbVq1ZTwmoPpL+ze8t1332H79u0ICgpCq1atkJDwYF6Kwslo35UrV2Lx4sVKkPv372/tbbkeQXmBF+cDgfn09W9VI+1ke4/KKWAe55QXa6qoWmP4mvu1PE8+8FUulAdDW5RVFYs2DG+CD9pWUNYrA3EPXbqNSauOqRKADT9fi48XRWHbqevSnFsQ3DFVhakpbEX21FNP4ZNPPlGWaKFChVTloYEDB6pI3CwNxMMDCxYsQMeOHdVrDosWKevlDhs2TO1j+HBERARmzpyJ7t27q64utIJ37NiB2rVrG+Zk6Ua+cOGCxc25XSJVJSMu7gJmPgsk3wVqvKivi8u5UeGR0JJkVC2DiDgXSlG0ND3lelwiVh+5ghWHYrDx+FVVjF4jNNAHzSpEoGXFCDQskx8BvhJwJAgun6ry9ddfw9vbG/Pnz8eUKVOUcBK6bFu3bo3sgqLMzi101Wrwpp588kls3bpVveaarlpNOAnP9/T0VJaqwGrotfRVh9hIe89sYP04e4/IaaBQ1iuVFx2qF1Jra/I68+byQ9faRVRT7j0ftcB3L9ZCp5qFkCfABzfuJmP+rgvo//Mu1PhkBfrP2qleS8CRILhwqkrRokWVe9SUSZMmITuhcBJamsbwtXaM6/Dw8HTHKex0IWvnmCMxMVEtGta6m52Ocq2BthOAxUOAdWP19XBrvmTvUbkNgb7eaF05Ui102bKbCy3SlVExuHjzHlZExaiF4lyneChaVoxEi4oRqv6uIAgulOfp7IwdOxZjxqQVFHAXavcFbl0ENo4HFg0CggsAZaSRdk7j7eWJ+qXyqYWBSZwXXZkmngwsYoF6LmzcXbFAbrSsRPduJCoUCFZTG4IgOAYOK56RkZFqHRMTo6JtNfiaeaXaOVeuXEn3c/fv31cRuNrPm+Pdd99Nl1ZDy7NIkSJweZp+ANy+COz7FZj7MtBnCVBQ/1kKOY8WcMRlSIuyqiWaskIPXcaOM7GIir6tli9WHUfh0AAlohTT2sVClQgLgmA/HFY8S5QooQRw9erVBrGkyHEuc8CAAep1vXr1cPPmTezatQu1atVS+1hvl4XqOTeaEez+4pYdYGi5tPsSuBMNnFqnb6T9yiogtJi9RyaktUnr16CEWmLjk7D6sN4i3XDsKi7cuIcfN59WiwQcCYITRttmJ8zHPHHihNquUaMGJk6cqDqzcM6Sc6vsG/rZZ5/hp59+UmL64YcfYv/+/YiKioK/vz6NoE2bNsoaZTpLcnIy+vTpowKI5syZY/E4XDra1hwJt4EZbYCYg0DeMkC/FUBgmL1HJWTA3aT72Hj8mponXX0kBjfvPkg5YheYRmXyo2WlSDQrH47QIF+7jlUQnBlrtMCu4rlu3Tqzbcx69eql0lE4tFGjRmHq1KnKwmQx+m+//RZly5Y1nEsXLVNkFi1apKJsn3/+eZUbmitXLovH4XbiSW5fAqa1AG5fAIrUBV7+C/CxrDSdYD8YcLTjzA2siLqsxJQBRxoMBmY6jQQc2Sb9SHB9bttSPFnYgNYg3amcb6SL1JhTp07B2XBL8SRXDgPTWwGJt4AK7YEuP0kjbSeC/3U5J0oR1QKOjKnAgKOKEWqelMFHGQUcuYugsA8rqz9Jg3PBLuLZo0cPrF+/Hi+99JIK5DH9Dzlo0CA4G24rnuT0RmB2JyAlCaj7H6D1WHuPSMgipgFHqUb/s1mHV4vcZTqMFnDkLoLC+2Qjc9MvO+3by7hylOC+3LaleLIowT///KMqDLkKbi2e5MB84I9++u1WnwL13rD3iITHxDjgiBWOEpIfeIhCGHBUPgL5gn0xdf0plxcUWtYNxq1J94BgjDQ4F7KiBVZH24aGhqqAHsGFqNJZPwe68kN9I23mgFbuZO9RCY9BWJAvutQuopZ7SSlKQCmkFFRWOPpj94UMf5ZiSgkZ9fchVC8SquZSU3Q6ZcmmpnKdts116oNtipRO29ZxW3+M+7lPl8G24VoWvIfZ12nX0pnb1ulUh5uMhNO0wTkrSQmCJVhtec6ePRt//fWXioANDHSNgAS3tzwJ/wyWjgD+/R7w8gVeWggUdx3vgvAg4Gjn2Rv4acsZLD2YcRUud2R4q3IY0LgUPMX6dFtu29Jty5SSkydPqie74sWLw8fHJ93x3bt3w9kQ8UwjNUVfPOHIYsA/D9B3BRBe3t6jEmzAX3svYtBvey06l1pCdybjG9S2WvO1fr9+2wOcRuW2Woy3PYz3G7022vZKu565n0333oZt/gweeu+HxuHhgcu37mGJhQ8KzKGl9ckKUA1K50OxvIFS2cmNuG1Lt63W9URw4UbaP7UHLvwL/NIZ6LcSyO38815CehhVawm/vlrX6V2ZdOPuGbcGl28lPDS/q+Hn7anEmC7tJQcuq0ULtKpfKi8alMmnPgdLPzfB9bFrnqejIJanCfHXgR9bAtdPAJFVgD5LAb9ge49KsEEQTUaC4mpBNFq0LdFlEBzFqk37L9zE5hPXsenENew5dwPJKek/nXIRwahfOi+eKpUPT5YMQ7B/es+b4Nw4TZEER0HE0wyxp4HpLYD4q0CppsALcwEv+aJwJSwRFFeIttWwNi2HlZ1YkGLziWtqYU6t8bclHyqqFc6Dp0rnU0uNoiHw85ZSic5Mtosno2vZ5Dpfvnwq2jazOQBW/HE2RDwz4OJuYGZbfSPt6j2BDt9II20Xw13yPLOjIATTf7aevI7NJ/Vievb63XTHWSrxiRK0SvMqMWVhCgk+cnPxZGRt9+7dVTF1bmcGS+s5GyKemXBsBfBrd0CXAjQeoV/ObgHiYoBcEUCx+vq5UsFpcZcKQ9kNU2C2pLl4t5y8hmtx6ZuZS/CR8yFuWysR8XwEu2bqe4AS/xAg4eaDY7kLAq3HARXb2214gmBv+DV6LCZOL6QnrmH76VjEJd5Pd44EHzk+Ip5WIuJpAXN7AVELzRxIe5LuOksEVBDSSE5JNQQf0cW720zwUdmIXPr5Ugk+chhEPK1ExNOC/M8vKuurEJnFQ2+BDj4gLlxBMIMWfESrlNapBB85JiKeViLiaUHx+J+effR5L8wDyrbMiREJglNjHHxEQT1jJvioTvEwNVcqwUc5h4inlYh4WlE4PlM8gPAKQMGaQKG0JbwS4C0NmgXhcYKPWMy/vpXBRxII5qDieeLECVWmr1GjRggICFAT5s4aSSbimU2Wpzm8/IACVdMEtZZ+CSspfUMFwYbBR+6WguQU4nn9+nV069YNa9asUWJ5/PhxlCxZEn379lU5oBMmTICzIeJp6ZxntEk6vcmcJ0v5Re8DLu4CLu3WrxNuPXy6Xx6gUA29kGqiKiUABSFbgo/uJCRj6Nx9Lt9qzunE8+WXX8aVK1cwbdo0VKhQAfv27VPiuXz5cgwdOhSHDh2CsyHiaQFRf+uLxmdUj8ZctC3/tGJP6YstaIJKcb1vpj1UcMEHrl4KasEaQECI7e5HEFw0+CgzXK3solOJZ2RkpBLKatWqITg42CCep06dQtWqVREXFwdnQ8TTCgFdNiJ91G3uQkDrzyxPU0lJBq5EPRBUrq8eBnQPmjUbyFsmTVDT3L0RlQEfyY0ThIyCj9ivNeZ24iN/5o0mpVQt32Jhgar3q7NOuTmVeFIw2XasTJky6cRz586daNWqlXLrOhsinla6cLO7wlBSfJq7VxPUXcDNsw+f5+kDRFRKE9M0Uc1XVtJjBCELreY0cvl5o2hYoApCKpY3SL8OC0TRvIEokCfArSzU27ZsSdawYUPMmjULn3zyiXrNJ5bU1FR8/vnnaNKkSdZHLTgHFKoSDbP3mr5BehHmYtzZRZs31UT17jUgeq9+2Tk97WdzAQWqG1moNYE8RaQGr+CWWFq1qHxkMG7eTcbl2wkqGImuXy6m+Hp5onBYgBJTCquxyBYJC3DrXFSrLc+DBw+iWbNmqFmzpgoaat++vZrnZEH4zZs3o1SpUnA2xPJ0AvhnevOckaDuAS7tAZLjHz43KH/66F4KamCYY1jZguBAreYSklNwPvauKnJ/5no8zqVtc830GdPApHTX8gAK5PZXFmqxsCC1Lp5muXI7txNWTLJ5qgov/PXXXyuXLec4KaRvvPEGChRwzgguEU8nheJ29aiRoO4CYg4BqenD+hWhxdNH9xaoBvgGWjm/K3V8BfdpNUchvnTznhLTs7HxOMe12uY6HneTUjL9eRbGL5o3CMUNbuAHLuH8wX7ZOs+aXTmtUiTBSkQ8XYjkBODygfSCyqbepnh46Qs6aO5eimp4RcDL2yiyOINgf6njKzg4ts7z1Ol0qpDDudh4vaCmWavKer1+F9fj0xd5MCXAx0tvoaa5gZWwhukt14Ih/vD28rTLvdpcPBMSErB//36VssL5TmPoxnU2RDxdnHs39S5elS6Ttr7DnFUTvAOAyCrAlUP6ICazSB1fwTmwZ4WhOwnJSkzPGVmqmshG37qH1ExUx9vTA4VCAwzCSkHVb+vXAb5eD1nZ2ZXTalPxXLZsmcr1vHbt2sMX8/BASkrmprwjIuLphtAda5x/yjnURDMFHTKi1+LsD5wSBDcg6X6qmk/Vi2m8EldNZCm4PJ4Z4cF+Bqt1+aGYh6ovPU5Oq03FkykqLVu2xEcffYSIiAi4AiKeAuhBiT0JbPsO2Dnt0efnr6Avgs9I34LVgdASEuErCI9JaqpORQDr3cBp1qqR5XonwbxQZsavr9ZVJQztnqoSExOjKgm5inAKgoK1dvOVASp1tEw8WdiBi4Z/Hn0QEsWUa1ZIoqBKDV9BsBh2jikYEqAWU8Gjncf0Gk1Mlx+8jCUHLz/ymnRb2wKrxbNz585Yt26dU6akCMIjYToK5zQzq+PLVJgm7wGX9wOX9uojfFnD9/QG/aLhlztNUNPElMIqRfEFIUtwWjA0yFct1YuEqHlcS8TT0txXq8djrdv27t276NKlC/Lnz48qVarAxyd9Ls9bb70FZ0PctsJj1fFVJQcP64s3UExZLSnmoPkavr7B+i4zmruX67ylRVAFwcY5rXaf85w+fTpef/11+Pv7I2/evOlydbjNGrfOhoinkO11fCmozEE1COpefQqNWUHNBUSybVuay5eCSheyRPMKQo7ktOZYYXhalyNHjoSnizwti3gKOVJhKOU+cO3oAzG9pAnqvYfP9QnSp81o1inXUsdXEJw3zzMsLAw7duxwqTlPEU/BrgJ97ZiJoO4Hku8+fK5PoL6zTDpBLacv7CAIbkyKM1QYGjJkiJrvfO+99+AqiHgKjieox9OK4O97IKhJcRkUdqicfg41PwXVirqiUsNXEGyfqsIiCOygwp6e7N9pGjA0ceJEZBejR4/GmDFj0u0rV64cjhw5Yqh09Pbbb+O3335DYmKiaon27bffShqN4NxQuMLL65dq3R8I3PWT6edQoymod4ALO/SLhre/vnWbsaCyFKE5QZUavoKQJawWzwMHDqBGjRqGDivG2KKhaqVKlbBq1SrDa29v73RW8D///IN58+app4WBAweiU6dOqruLILicoOYvq1+qdk1f2EFZp3v0ay6Jtx/U9dXw8tMLqnFQUuwpYH7fh1NymKbDaGNXrOHrTla2O92rHXDowvC0PBcuXIi9ex9u7kqzmu7jOXPmqNxTQou0QoUK2Lp1K+rWrWvx+4jbVnAZKKg3TqeJqWal7reu9KChhm8B4K39gLfztZYyiztZ2e50r87its1pjh8/joIFC6rUmHr16mHs2LEoWrQodu3aheTkZDRv3txwbvny5dUxa8VTEFwGRsDnLaVfqnR+0AtVCapRUNKFneZ7oRrQ6b94/5tPP6/qwyUwbW28be2+DI7RpWzL8oYZdcpxRSvbne7VjlgknnSFzpw5UykxtzPjzz//zK6x4cknn1Tvy3nO6OhoNf/ZsGFD5S6+fPkyfH19ERISku5nON/JY5nB+VEuxk8bguCyUJRY2YhL5bT/v/vnAX++YtnPM5WGy71YG47Ry3biTJf10ncyqBjFfR7AspFA+bbO79akq5YWpzvcqzOIJ81YbT6T2zlFmzZtDNsMTqKYFitWDHPnzkVAQECWr0vr1TQQSRDciuBIy87rOltfESn5nj59Rq2Nt03X1uyLB3RpHTR0KfrgJy45Dq3si8D4svpgK/Vd55GWae9h9FqzjE33ZbY2OV+9tPRnTd7Xkve/dyO9qzajez20AKjQDvD2s+Hn6tpYPOf58ccfY9iwYQgMDIQ9qVOnjnLVtmjRAs2aNcONGzfSWZ8U18GDB6tgImsszyJFisicp+A+0EL5onLmNXxt3beUXz2sxGSR2GZFnNO2zRWhEPQEhALBBfQBRVwHR5i8jtRv+9imPqxbzHnSUmNZPnuKZ1xcHE6ePImXXnoJtWrVUmkyq1evxvPPP6+OHz16FOfOnVNzo5nh5+enFkFwWyiIDB5Rc2Me5oubsRShLV17tJa8ffVLQPrpl2zl1AZgVrtHn/fsJH0UMj8L9XFwrbNg/ahzrbmW0Vr9mMn7POpnWBJyy5ePvldPHyA1WW+pcrkS9WiRzRWpF1PDUiC96PK4vUTWDpHFFounPYJyaem2a9dOWZOXLl3CqFGj4OXlhR49eqing379+qn2aKx6xKeEN998UwmnBAsJggUwaITBI2ajMi2s4esMFH/q0Z1yeLxmL+efB6SIHJz/6HsdxAjs23qxuRMN3Ln8YIkz2uaSkvhAZI3b8JnDP+SBxaotpqKb3SJrp8hiq6JtbZHHmRkXLlxQQnn9+nWVltKgQQNs27ZNbZNJkyap+rq0PI2LJAiCYCH8cmHwiCvnAzqCle1o98qSjoFh+oUFNDKCRhNF0yCyaes4M6JLkU24qV8sFlkzbmJrRNaOkcUWz3lSpIwDhzIiNtaGEXk2QvI8BcENeNxOOc5ETt+rTqcXTSWk5kTWSGwpspaiRDYDN3FQODC/j/49zGL9vL1NattSPL/44otHRtv26tULzoaIpyC4Ce5UdccR71VnLLKmbmIT0TXXvi8r9FoMlGho3yIJ3bt3R3h4uDU/IgiC4DhQPCz8InV6HPFePTz0wUdcHuUuViJrZLGazsVePwHcvfbo98zQMn08vB11vlMQBEFwUzyMRba8+XNObwR+evbR16LVbQMs7mbtwCVwBUEQBHejWH39nKYWCGV2zrOQ/jx7imdqaqq4bAVBEATHiixWmAqo7aOoLRZPQRAEQXDIXOXcBdLvp0Vq4wL4Dt9VRRAEQRAcLVdZxFMQBEFwbjxzPrJY3LaCIAiCYCUinoIgCIJgJSKegiAIgmAlMudplMPK0kyCIAiCe3I7TQMsqWsg4gngzh1993o2xBYEQRDcmzt37jyyjrvFheFdGRaAYL/Q4ODgLJch5BMLxff8+fMuX1xe7tX1cJf7JHKvrsntbLhXyiGFs2DBgqoZSmaI5ZnWMaZw4cLZci3+0lz9j1RD7tX1cJf7JHKvrknux7zXR1mcGhIwJAiCIAhWIuIpCIIgCFYi4plN+Pn5YdSoUWrt6si9uh7ucp9E7tU18cvhe5WAIUEQBEGwErE8BUEQBMFKRDwFQRAEwUpEPAVBEATBSkQ8BUEQBMFKRDwfkw0bNqBdu3aqIgWrEy1cuBCuytixY1GnTh1ViSk8PBwdO3bE0aNH4WpMmTIFVatWNSRb16tXD0uXLoU78Nlnn6m/48GDB8PVGD16tLo346V8+fJwRS5evIgXX3wRefPmRUBAAKpUqYKdO3fC1ShevPhDv1Mub7zxhs3fW8TzMYmPj0e1atXwzTffwNVZv369+qPctm0bVq5cieTkZLRs2VJ9Bq4Eq01RRHbt2qW+cJo2bYoOHTrg0KFDcGV27NiB77//Xj04uCqVKlVCdHS0Ydm0aRNcjRs3buCpp56Cj4+PeuiLiorChAkTEBoaClf8m402+n3ye4l06dLF9m/OVBUhe+DHuWDBAp27cOXKFXXP69ev17k6oaGhumnTpulclTt37ujKlCmjW7lypa5x48a6QYMG6VyNUaNG6apVq6ZzdUaMGKFr0KCBzh0ZNGiQrlSpUrrU1FSbv5dYnkKWuXXrllqHhYXBVUlJScFvv/2mrGu6b10VehTatm2L5s2bw5U5fvy4mmIpWbIkevbsiXPnzsHV+Pvvv1G7dm1lfXF6pUaNGvjhhx/g6iQlJWH27Nno27dvlht8WIMUhhey3ImG82J0D1WuXBmuxoEDB5RYJiQkIFeuXFiwYAEqVqwIV4QPB7t371YuMFfmySefxMyZM1GuXDnl4hszZgwaNmyIgwcPqnl8V+HUqVNq3n7o0KF477331O/1rbfegq+vL3r16gVXZeHChbh58yZ69+6dI+8n4ilk2VLhl44rzhkRfsHu3btXWdfz589XXzqc83U1AWX7pkGDBqm5In9/f7gybdq0MWxzXpdiWqxYMcydOxf9+vWDKz3Y0vL89NNP1Wtanvy/+t1337m0eE6fPl39julZyAnEbStYzcCBA7F48WKsXbs221q5ORp8Si9dujRq1aqloowZFDZ58mS4GgyKunLlCmrWrAlvb2+18CHhyy+/VNt0W7sqISEhKFu2LE6cOAFXokCBAg895FWoUMElXdQaZ8+exapVq/DKK68gpxDLU7AYxkS9+eabyoW5bt06lChRAu4Cn+YTExPhajRr1ky5qI3p06ePSuEYMWIEvLy84KrExcXh5MmTeOmll+BKcCrFNIXs2LFjysp2VWbMmKHmdzlvn1OIeGbDf0DjJ9fTp08rdx+DaIoWLQpXc9XOmTMHf/31l5ojunz5sqF5LHPJXIV3331XuX/4+2NXed4zHxaWL18OV4O/R9M566CgIJUf6Gpz2cOGDVM52RSRS5cuqQ4cfDjo0aMHXIkhQ4agfv36ym3btWtX/Pvvv5g6dapaXPXBdsaMGcolTW9JjmHzeF4XZ+3atSpdw3Tp1auXztUwd59cZsyYoXMl+vbtqytWrJjO19dXlz9/fl2zZs10K1as0LkLrpqq0q1bN12BAgXU77VQoULq9YkTJ3SuyKJFi3SVK1fW+fn56cqXL6+bOnWqzlVZvny5+h46evRojr6vtCQTBEEQBCuRgCFBEARBsBIRT0EQBEGwEhFPQRAEQbASEU9BEARBsBIRT0EQBEGwEhFPQRAEQbASEU9BEARBsBIRT0FwEs6cOaNaLbGClaNw5MgR1K1bVxWVr169ur2HIwg5hoinIFgIWx1RvD777LOHWiHlRP9AR4Ql7ljOj7VUV69eneF5LOXI7i0stk+hjYiIUDVY2Trr7t27hvOKFy+uPksuvC4L1s+bNy/d76Bjx44PXZ/lE/kzbEklCDmBiKcgWAG/+MeNG4cbN27AlZoIZxUWVm/QoIGqF8t6uBn1l2RbrBUrVqh6q3v27MHWrVvxzjvvqO487IZhzMcff6z6bfK8OnXqoFu3btiyZUuWxygItkDEUxCsoHnz5oiMjFRtyjJi9OjRD7kwv/jiC2VVmVpQFBNaYWyPRdG4f/8+hg8frhoLsN0bC16bc5Wy8DeFnMXb2ULMGPZuZGF7NvHmtdk15Nq1a4bjTz/9tGorx2bm+fLlQ6tWrTIsuM0xcRx+fn7qnpYtW2Y4TkuPLc14Drd53+b4z3/+owp279y5UxUqZ3uskiVLokOHDvjnn39UsXbTYvX8jNku7JtvvlFNBxYtWgRr2LdvH5o0aaKulTt3btVaju8vCNmFiKcgWAG7cFDwvvrqK1y4cOGxrrVmzRrV3WPDhg2YOHGicoE+++yzCA0Nxfbt2/H666/jtddee+h9KK5vv/22sszq1aunxOf69evqGN2WTZs2VZYexYJiFxMTo0TLmJ9++kn1LN28ebNqkmwO9i+dMGECxo8fj/379yuRbd++PY4fP66O0zqsVKmSGgu32bXEFI6LFic78tANa47MXN4UXR8fH6ut4549eyrR37FjhxL4kSNHqusIQnYh4ikIVvLcc88pK4xi9zjQumTT6XLlyqFv375qzfm/9957D2XKlFGt0ShwmzZtSvdztBqff/55ZcFxzpAt4aZPn66Off3110o4KfDsycntH3/8UTUuZ09HDV7/888/V+/JxRwUTfb07N69uzqH7mreN61oQuuQ4kYLl9tcm8J2few9YfoetHh5Phe+hzkomLTwb926pR4IrIGNn+kl4GfAe+3SpYtqaC4I2YWIpyBkAQoJrbfDhw9n+Rq02jw9H/wXpIu1SpUq6axcziNeuXIl3c/R2tSgeNWuXdswDrorKZSaMHGhgGjzkxp0Y2bG7du3lVXMoB5j+Ppx7lmDPSYZNczPwLTJOMWU4w4MDFSfMwO0rG1yPHToULzyyitKQPnzxvcuCNmBiKcgZIFGjRopNyatQ1MoiKad/pKTkx86z9SNSPeluX2ce7SmOTvduBQm44WuVo5ZIyMXanbD6FreA6NxjeGcJ4+Za6JOtzTHTHc1A7OMLVPOX9ISNYXuaj5saPfF+ddDhw4p0aV7vGLFiliwYIFN7lFwT0Q8BSGL0KJhIAsjR43Jnz+/Ss0wFtDszM3ctm2bYZsBRpzTowuXMLWDosHgJIqT8WKNYFKkChYsqOZEjeFrCpGl0HJu0aKFcifHx8db9DN06XK8dAWbzofS/cv7M7VWd+/ejRIlSqR7+GDA0ZAhQ9Sca6dOncwGXwlCVhHxFIQsQhcrA1M4b2kMo1mvXr2q5hTpLmTE6NKlS7PtfXk9WlGMumUgDq0zzpkSvo6NjUWPHj1UsAzff/ny5ejTpw9SUlKseh9agHSb/v7778pyZNANHwKYr2kN3377rRJ5upd5Lbp9eb3Zs2ere6DFaCn8vCmoL7/8snpo4Jwq53Q5D8vAJXLv3j01L8zcz7NnzyrB52ehPWAIQnYg4ikIjwHTNEzdqvySpmBQ5Bikwvk9c5Goj2PxcuG1GUz0999/K2uNaNYihbJly5ZK4JmSwlQY4/lVS3jrrbfU3CFFiddh5C7fiwE41lCqVCkVGcz5R7q5OW4KKSOW+bl88sknFl+L97Fx40blBmfkLwOY+PDCaGVGJhOKMaN8KbC0PhlpzNSdMWPGWDVuQcgMD53p5IwgCIIgCJkilqcgCIIgWImIpyAIgiBYiYinIAiCIFiJiKcgCIIgWImIpyAIgiBYiYinIAiCIFiJiKcgCIIgWImIpyAIgiBYiYinIAiCIFiJiKcgCIIgWImIpyAIgiBYiYinIAiCIMA6/h8VCD4AQxYeFQAAAABJRU5ErkJggg==", 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", 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" ] @@ -430,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -445,22 +432,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -484,22 +471,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -524,22 +511,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -563,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -578,7 +565,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -600,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -615,7 +602,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] From 15188943a2f56dea09f83b09e5684e3ab6e47abf Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 3 Jul 2025 20:01:46 +0000 Subject: [PATCH 60/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- ...bix_pipeline_single_function_scaling.ipynb | 335 ++---------------- rubix/core/pipeline.py | 8 +- 2 files changed, 30 insertions(+), 313 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index aff63c30..45fc46bf 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -14,17 +14,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5), CudaDevice(id=6), CudaDevice(id=7)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -46,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -84,26 +76,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-03 12:16:01,439 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-07-03 12:16:01,440 - rubix - INFO - Rubix version: 0.0.post447+g8128662.d20250605\n", - "2025-07-03 12:16:01,440 - rubix - INFO - JAX version: 0.6.0\n", - "2025-07-03 12:16:01,441 - rubix - INFO - Running on [CudaDevice(id=0), CudaDevice(id=1), CudaDevice(id=2), CudaDevice(id=3), CudaDevice(id=4), CudaDevice(id=5), CudaDevice(id=6), CudaDevice(id=7)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -186,18 +161,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -205,32 +171,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-03 12:16:02,261 - rubix - INFO - Getting rubix data...\n", - "2025-07-03 12:16:02,263 - rubix - INFO - Rubix galaxy file already exists, skipping conversion\n", - "2025-07-03 12:16:02,338 - rubix - INFO - Centering stars particles\n", - "2025-07-03 12:16:04,253 - rubix - WARNING - The Subset value is set in config. Using only subset of size 1 for stars\n", - "2025-07-03 12:16:04,254 - rubix - INFO - Data loaded with 1 star particles and 0 gas particles.\n", - "2025-07-03 12:16:04,255 - rubix - INFO - Data preparation completed in 1.99 seconds.\n" - ] - }, - { - "data": { - "text/plain": [ - "(1, 3)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import jax.numpy as jnp\n", @@ -253,57 +196,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-03 12:16:04,269 - rubix - INFO - Setting up the pipeline...\n", - "2025-07-03 12:16:04,270 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-07-03 12:16:04,271 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-07-03 12:16:04,274 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:04,293 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:04,700 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:04,715 - rubix - INFO - Getting cosmology...\n", - "2025-07-03 12:16:04,784 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:04,948 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:05,169 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:05,327 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:05,811 - rubix - INFO - Assembling the pipeline...\n", - "2025-07-03 12:16:05,812 - rubix - INFO - Compiling the expressions...\n", - "2025-07-03 12:16:05,813 - rubix - INFO - Number of devices: 8\n", - "2025-07-03 12:16:06,991 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-07-03 12:16:07,107 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-07-03 12:16:07,112 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-07-03 12:16:07,128 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-07-03 12:16:07,279 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-07-03 12:16:07,279 - rubix - INFO - Convolving with PSF...\n", - "2025-07-03 12:16:07,283 - rubix - INFO - Convolving with LSF...\n", - "2025-07-03 12:16:07,289 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-07-03 12:16:24,983 - rubix - INFO - Sharding completed in 2.57 seconds.\n", - "2025-07-03 12:16:24,987 - rubix - INFO - Sharded pipeline run completed in 18.15 seconds.\n", - "2025-07-03 12:16:24,988 - rubix - INFO - Total time for sharded pipeline run: 20.71 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -312,64 +207,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-07-03 12:16:25,002 - rubix - INFO - Setting up the pipeline...\n", - "2025-07-03 12:16:25,005 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-07-03 12:16:25,006 - rubix - DEBUG - Roataion Type found: edge-on\n", - "2025-07-03 12:16:25,009 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:25,033 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:25,054 - rubix - INFO - Calculating spatial bin edges...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:25,072 - rubix - INFO - Getting cosmology...\n", - "2025-07-03 12:16:25,164 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:25,241 - rubix - DEBUG - SSP Wave: (5994,)\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:25,262 - rubix - INFO - Getting cosmology...\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:25,355 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/export/home/aschaibl/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /export/home/aschaibl/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-07-03 12:16:25,437 - rubix - INFO - Assembling the pipeline...\n", - "2025-07-03 12:16:25,437 - rubix - INFO - Compiling the expressions...\n", - "2025-07-03 12:16:25,439 - rubix - INFO - Number of devices: 8\n", - "2025-07-03 12:16:25,622 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-07-03 12:16:25,718 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-07-03 12:16:25,722 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-07-03 12:16:25,724 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-07-03 12:16:25,735 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-07-03 12:16:25,736 - rubix - INFO - Convolving with PSF...\n", - "2025-07-03 12:16:25,740 - rubix - INFO - Convolving with LSF...\n", - "2025-07-03 12:16:25,745 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-07-03 12:16:40,686 - rubix - INFO - Sharding completed in 0.44 seconds.\n", - "2025-07-03 12:16:40,687 - rubix - INFO - Sharded pipeline run completed in 15.24 seconds.\n", - "2025-07-03 12:16:40,688 - rubix - INFO - Total time for sharded pipeline run: 15.68 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "rubixdata = pipe.run_sharded(inputdata)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -381,30 +228,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5,3))\n", @@ -417,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -432,30 +258,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", @@ -471,30 +276,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", @@ -511,30 +295,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", @@ -550,30 +313,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Time in seconds on M1')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", @@ -587,30 +329,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Time per particle in seconds')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index a3f41af1..94f96573 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -86,9 +86,7 @@ def prepare_data(self): f"Data loaded with {star_count} star particles and {gas_count} gas particles." ) t2 = time.time() - self.logger.info( - "Data preparation completed in %.2f seconds.", t2 - t1 - ) + self.logger.info("Data preparation completed in %.2f seconds.", t2 - t1) return rubixdata @jaxtyped(typechecker=typechecker) @@ -327,9 +325,7 @@ def _shard_pipeline(sharded_rubixdata): sharded_result = sharded_pipeline(inputdata) time_end = time.time() - self.logger.info( - "Sharding completed in %.2f seconds.", time_mid - time_start - ) + self.logger.info("Sharding completed in %.2f seconds.", time_mid - time_start) self.logger.info( "Sharded pipeline run completed in %.2f seconds.", time_end - time_mid ) From 090a4e22ca6246817e13880487a0687598dd5e14 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Thu, 3 Jul 2025 22:36:58 +0200 Subject: [PATCH 61/76] fix failing test after merge. --- rubix/galaxy/alignment.py | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/rubix/galaxy/alignment.py b/rubix/galaxy/alignment.py index 5be4603a..2b2e1041 100644 --- a/rubix/galaxy/alignment.py +++ b/rubix/galaxy/alignment.py @@ -239,7 +239,7 @@ def rotate_galaxy( alpha: float, beta: float, gamma: float, - R=None, # type: Float[Array, "3 3"] = None + key: str, ) -> Tuple[Float[Array, "* 3"], Float[Array, "* 3"]]: """ Orientate the galaxy by applying a rotation matrix to the positions of the particles. @@ -252,21 +252,30 @@ def rotate_galaxy( alpha (float): Rotation around the x-axis in degrees beta (float): Rotation around the y-axis in degrees gamma (float): Rotation around the z-axis in degrees + key (str): The key to the particle data, e.g. "IllustrisTNG" or "NIHAO" Returns: The rotated positions and velocities as a jnp.ndarray. """ - if R is None: - I = moment_of_inertia_tensor(positions, masses, halfmass_radius) + # we have to distinguis between IllustrisTNG and NIHAO. + # The nihao galaxies are already oriented face-on in the pynbody input handler. + # The IllustrisTNG galaxies are not oriented face-on, so we have to calculate the moment of inertia tensor + # and apply the rotation matrix to the positions and velocities. + # After that the simulations can be treated in the same way. + # Then the user specific rotation is applied to the positions and velocities. + if key == "IllustrisTNG": + I = moment_of_inertia_tensor(positions_stars, masses_stars, halfmass_radius) R = rotation_matrix_from_inertia_tensor(I) pos_rot = apply_init_rotation(positions, R) vel_rot = apply_init_rotation(velocities, R) pos_final = apply_rotation(pos_rot, alpha, beta, gamma) vel_final = apply_rotation(vel_rot, alpha, beta, gamma) + elif key == "NIHAO": + pos_final = apply_rotation(positions, alpha, beta, gamma) + vel_final = apply_rotation(velocities, alpha, beta, gamma) else: - pos_rot = apply_init_rotation(positions, R) - vel_rot = apply_init_rotation(velocities, R) - pos_final = apply_rotation(pos_rot, alpha, beta, gamma) - vel_final = apply_rotation(vel_rot, alpha, beta, gamma) + raise ValueError( + f"Unknown key: {key} for the rotation. Supported keys are 'IllustrisTNG' and 'NIHAO'." + ) return pos_final, vel_final From 20fe06c973ee5af17a48c36a93e60d14a08b7dd2 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Thu, 3 Jul 2025 22:43:31 +0200 Subject: [PATCH 62/76] updated notebook cells with `#NBVAL_SKIP` --- .../rubix_pipeline_single_function_scaling.ipynb | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index 45fc46bf..a97e9671 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -211,6 +211,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "rubixdata = pipe.run_sharded(inputdata)" ] }, @@ -220,6 +221,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import jax.numpy as jnp\n", "gpu_number = jnp.array([1, 2, 3, 4, 5, 6, 7])\n", "time_on_compgpu4_5e5mal2 = jnp.array([274.27, 152.38, 108.70, 88.38, 88.97, 71.85, 62.91])\n", @@ -232,6 +234,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5,3))\n", "plt.plot(gpu_number, time_on_compgpu4_5e5mal2, marker='o', label='1e6 particles')\n", @@ -247,6 +250,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import jax.numpy as jnp\n", "particle_number = jnp.array([1, 10, 100, 1e3, 1e4, 1e5, 5e5, 5e5*2, 5e5*20, 5e5*50, 5e5*100, 5e5*150, 5e5*200, 5e5*300, 5e5*400])\n", "time_on_mac_2cpu = jnp.array([2.14, 2.14, 2.24, 2.2, 2.2 ,2.15, 2.34, 2.26, 2.50, 3.78, 16.88, 38.92, 56.29, 72.27, 86.98]) #seconds\n", @@ -262,6 +266,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu, marker='o', label='2 GPUs')\n", @@ -280,6 +285,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu, marker='o', label='2 GPUs')\n", @@ -299,6 +305,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu/particle_number_gpu, marker='o', label='2 GPUs')\n", @@ -317,6 +324,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", "plt.plot(particle_number, time_on_mac_2cpu, marker='o', linestyle='-')\n", @@ -324,7 +332,7 @@ "plt.yscale('log')\n", "plt.xlabel('Number of particles')\n", "plt.ylabel('Time in seconds on M1')\n", - "#plt.title('Scaling of Rubix Pipeline with Number of Particles')" + "# plt.title('Scaling of Rubix Pipeline with Number of Particles')" ] }, { @@ -333,6 +341,7 @@ "metadata": {}, "outputs": [], "source": [ + "# NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(5, 3))\n", "plt.plot(particle_number, time_on_mac_2cpu/particle_number, marker='o', linestyle='-')\n", @@ -340,7 +349,7 @@ "plt.yscale('log')\n", "plt.xlabel('Number of particles')\n", "plt.ylabel('Time per particle in seconds')\n", - "#plt.title('Scaling of Rubix Pipeline with Number of Particles')" + "# plt.title('Scaling of Rubix Pipeline with Number of Particles')" ] } ], From f0759af52ea0bbd17622752b34b5d7abe56052b4 Mon Sep 17 00:00:00 2001 From: anschaible Date: Fri, 4 Jul 2025 12:31:12 +0200 Subject: [PATCH 63/76] change pipeline config name --- notebooks/debug_spectra_lookup.ipynb | 222 --- notebooks/pipeline_sharding_test.ipynb | 1507 ----------------- ...bix_pipeline_single_function_scaling.ipynb | 4 +- ...x_pipeline_single_function_shard_map.ipynb | 4 +- ...eline_single_function_shard_map_fits.ipynb | 8 +- ...ine_single_function_shard_map_memory.ipynb | 8 +- rubix/config/pipeline_config.yml | 76 +- 7 files changed, 25 insertions(+), 1804 deletions(-) delete mode 100644 notebooks/debug_spectra_lookup.ipynb delete mode 100644 notebooks/pipeline_sharding_test.ipynb diff --git a/notebooks/debug_spectra_lookup.ipynb b/notebooks/debug_spectra_lookup.ipynb deleted file mode 100644 index 1ef429d0..00000000 --- a/notebooks/debug_spectra_lookup.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "0", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import os\n", - "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1, 2, 3, 4 '\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1", - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "from rubix.core.pipeline import RubixPipeline \n", - "import os\n", - "config = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"IllustrisAPI\",\n", - " \"args\": {\n", - " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", - " \"particle_type\": [\"stars\"],\n", - " \"simulation\": \"TNG50-1\",\n", - " \"snapshot\": 99,\n", - " \"save_data_path\": \"data\",\n", - " },\n", - " \n", - " \"load_galaxy_args\": {\n", - " \"id\": 14,\n", - " \"reuse\": True,\n", - " },\n", - " \n", - " \"subset\": {\n", - " \"use_subset\": True,\n", - " \"subset_size\": 400000,\n", - " },\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"IllustrisTNG\",\n", - " \"args\": {\n", - " \"path\": \"data/galaxy-id-14.hdf5\",\n", - " },\n", - " \n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.1,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"FSPS\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import jax\n", - "import jax.numpy as jnp\n", - "\n", - "n_particles = 400_000\n", - "\n", - "age = jnp.linspace(0, 20, n_particles, )\n", - "metallicity = jnp.linspace(0., 0.05, n_particles, )\n", - "\n", - "from jax.sharding import Mesh, PartitionSpec as P\n", - "from jax.experimental import shard_map\n", - "from jax.sharding import NamedSharding\n", - "\n", - "\n", - "\n", - "devices = jax.devices()\n", - "mesh = Mesh(devices, axis_names=('N_particles',))\n", - "sharding = NamedSharding(mesh, P('N_particles')) \n", - "\n", - "age = jax.device_put(age, sharding)\n", - "metallicity = jax.device_put(metallicity, sharding)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "age = jnp.atleast_1d(age)\n", - "metallicity = jnp.atleast_1d(metallicity)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.core.ssp import get_lookup_interpolation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "lookup_interpolation = get_lookup_interpolation(config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "print(\"lookup_interpolation\", lookup_interpolation)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def lookup_interpolation_lax(age_metallicity):\n", - " age, metallicity = age_metallicity\n", - " return lookup_interpolation(age, metallicity)\n", - "\n", - "interpolation = jax.lax.map(lookup_interpolation_lax, (age, metallicity), batch_size=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "_, interpolation = jax.lax.scan(\n", - " lambda carry, x: (carry, lookup_interpolation_lax(x)),\n", - " None,\n", - " (age, metallicity),\n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/pipeline_sharding_test.ipynb b/notebooks/pipeline_sharding_test.ipynb deleted file mode 100644 index d12f2654..00000000 --- a/notebooks/pipeline_sharding_test.ipynb +++ /dev/null @@ -1,1507 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "0", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "\n", - "import os\n", - "import multiprocessing\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "# Logical cores (includes hyperthreads)\n", - "print(\"Logical cores:\", os.cpu_count())\n", - "\n", - "\n", - "# Total threads/cores via multiprocessing\n", - "print(\"multiprocessing.cpu_count():\", multiprocessing.cpu_count())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "# use dotenv to handle env variables\n", - "import os\n", - "from dotenv import load_dotenv\n", - "env_loaded =load_dotenv(dotenv_path='./data.env')\n", - "assert env_loaded, \"Failed to load .env file\"\n", - "\n", - "import jax.numpy as jnp\n", - "import jax\n", - "from jax.sharding import PartitionSpec as P, NamedSharding\n", - "\n", - "from rubix.core.pipeline import RubixPipeline \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "print(jax.devices())\n" - ] - }, - { - "cell_type": "markdown", - "id": "4", - "metadata": {}, - "source": [ - "# RUBIX pipeline\n", - "\n", - "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execute the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline on multiple machines. To see, how the pipeline is executed in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", - "\n", - "## How to use the Pipeline\n", - "1) Define a `config`\n", - "2) Setup the `pipeline yaml`\n", - "3) Run the RUBIX pipeline\n", - "4) Do science with the mock-data" - ] - }, - { - "cell_type": "markdown", - "id": "5", - "metadata": {}, - "source": [ - "## Step 1: Config\n", - "\n", - "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", - "\n", - "For the `config` you can choose the following options:\n", - "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", - "- `logger`: RUBIX has implemented a logger to report to the user, what is happening during the pipeline execution and give warnings\n", - "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", - "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", - "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", - "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", - "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", - "- `data - load_galaxy_args - reuse`: if True, if in the save_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", - "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", - "- `simulation - name`: currently only IllustrisTNG is supported\n", - "- `simulation - args - path`: where the data is stored and how the file will be named\n", - "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", - "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", - "- `telescope - psf`: define the point spread function that is applied to the mock data\n", - "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", - "- `telescope - noise`: define the noise that is applied to the mock data\n", - "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", - "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", - "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", - "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6", - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "\n", - "\n", - "config = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"IllustrisAPI\",\n", - " \"args\": {\n", - " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", - " \"particle_type\": [\"stars\"],\n", - " \"simulation\": \"TNG50-1\",\n", - " \"snapshot\": 99,\n", - " \"save_data_path\": \"data\",\n", - " },\n", - " \n", - " \"load_galaxy_args\": {\n", - " \"id\": 14,\n", - " \"reuse\": True,\n", - " },\n", - " \n", - " \"subset\": {\n", - " \"use_subset\": True,\n", - " \"subset_size\": 30000,\n", - " },\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"IllustrisTNG\",\n", - " \"args\": {\n", - " \"path\": \"data/galaxy-id-14.hdf5\",\n", - " },\n", - " \n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.1,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"FSPS\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "7", - "metadata": {}, - "source": [ - "## Step 2: Pipeline yaml\n", - "\n", - "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", - "\n", - "```yaml\n", - "calc_ifu:\n", - " Transformers:\n", - " rotate_galaxy:\n", - " name: rotate_galaxy\n", - " depends_on: null\n", - " args: []\n", - " kwargs:\n", - " type: \"face-on\"\n", - " filter_particles:\n", - " name: filter_particles\n", - " depends_on: rotate_galaxy\n", - " args: []\n", - " kwargs: {}\n", - " spaxel_assignment:\n", - " name: spaxel_assignment\n", - " depends_on: filter_particles\n", - " args: []\n", - " kwargs: {}\n", - " reshape_data:\n", - " name: reshape_data\n", - " depends_on: spaxel_assignment\n", - " args: []\n", - " kwargs: {}\n", - " calculate_spectra:\n", - " name: calculate_spectra\n", - " depends_on: reshape_data\n", - " args: []\n", - " kwargs: {}\n", - " scale_spectrum_by_mass:\n", - " name: scale_spectrum_by_mass\n", - " depends_on: calculate_spectra\n", - " args: []\n", - " kwargs: {}\n", - " doppler_shift_and_resampling:\n", - " name: doppler_shift_and_resampling\n", - " depends_on: scale_spectrum_by_mass\n", - " args: []\n", - " kwargs: {}\n", - " calculate_datacube:\n", - " name: calculate_datacube\n", - " depends_on: doppler_shift_and_resampling\n", - " args: []\n", - " kwargs: {}\n", - " convolve_psf:\n", - " name: convolve_psf\n", - " depends_on: calculate_datacube\n", - " args: []\n", - " kwargs: {}\n", - " convolve_lsf:\n", - " name: convolve_lsf\n", - " depends_on: convolve_psf\n", - " args: []\n", - " kwargs: {}\n", - " apply_noise:\n", - " name: apply_noise\n", - " depends_on: convolve_lsf\n", - " args: []\n", - " kwargs: {}\n", - "```\n", - "\n", - "There is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" - ] - }, - { - "cell_type": "markdown", - "id": "8", - "metadata": {}, - "source": [ - "# Data organization" - ] - }, - { - "cell_type": "markdown", - "id": "9", - "metadata": {}, - "source": [ - "try simple approach for this thing for now. This is really stupid: just build a giant box of zeros, index into them in the right way, and use these indices to assign the values we want to slices in the box" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "\n", - "# this function builds the data from the rubixdata object because that is easiest, but should not really be done imho. \n", - "def build_data(inputdata): \n", - " long_axis = inputdata.stars.age.shape[0]\n", - " data = jnp.zeros((long_axis, 6200), dtype=jnp.float32)\n", - " inputdata.galaxy.redshift = jnp.float32(inputdata.galaxy.redshift)\n", - " inputdata.galaxy.halfmassrad_stars = jnp.array(inputdata.galaxy.halfmassrad_stars, dtype=jnp.float32)\n", - " inputdata.galaxy.center = jnp.array(inputdata.galaxy.center, dtype=jnp.float32)\n", - "\n", - " inputdata.stars.coords = jnp.array(inputdata.stars.coords, dtype=jnp.float32)\n", - " inputdata.stars.age = jnp.array(inputdata.stars.age, dtype=jnp.float32)\n", - " inputdata.stars.velocity = jnp.array(inputdata.stars.velocity, dtype=jnp.float32)\n", - " inputdata.stars.metallicity = jnp.array(inputdata.stars.metallicity, dtype=jnp.float32)\n", - " inputdata.stars.mass = jnp.array(inputdata.stars.mass, dtype=jnp.float32)\n", - " # stars properties\n", - " data = data.at[:, 0:3].set(inputdata.stars.coords)\n", - " data = data.at[:, 3:6].set(inputdata.stars.velocity)\n", - " data = data.at[:, 6].set(inputdata.stars.metallicity)\n", - " data = data.at[:, 7].set(inputdata.stars.age)\n", - " data = data.at[:, 8].set(inputdata.stars.mass)\n", - "\n", - " # galaxy properties\n", - " data = data.at[:, 9].set(inputdata.galaxy.halfmassrad_stars)\n", - " data = data.at[:, 10].set(inputdata.galaxy.redshift)\n", - " data = data.at[:, 11:14].set(inputdata.galaxy.center)\n", - " \n", - " mesh = jax.make_mesh((jax.device_count(), ), ('x',))\n", - " shard = NamedSharding(mesh, P('x'))\n", - "\n", - " data = jax.device_put(data, shard)\n", - "\n", - " return data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "def stars(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Stars function to be used in the pipeline.\n", - " \"\"\"\n", - " # Perform some operations on the data\n", - " # For example, let's just return the data as is\n", - " return data[:, 0:9]\n", - "\n", - "def gas(data: jnp.ndarray) -> jnp.ndarray:\n", - " return data # index after adjusting the above for gas\n", - "\n", - "def galaxy(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Galaxy function to be used in the pipeline.\n", - " \"\"\"\n", - " # Perform some operations on the data\n", - " # For example, let's just return the data as is\n", - " return data[:, 9:14]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "def coords_idx(): \n", - " return jnp.s_[:, 0:3]\n", - "\n", - "def coords(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Coords function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[coords_idx()]\n", - "\n", - "def velocity_idx():\n", - " return jnp.s_[:, 3:6]\n", - "\n", - "def velocity(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Velocity function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[velocity_idx()]\n", - "\n", - "def metallicity_idx():\n", - " return jnp.s_[:, 6]\n", - "\n", - "def metallicity(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Metallicity function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[metallicity_idx()]\n", - "\n", - "def age_idx():\n", - " return jnp.s_[:, 7]\n", - "\n", - "def age(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Age function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[age_idx()]\n", - "\n", - "def mass_idx():\n", - " return jnp.s_[:, 8]\n", - "\n", - "def mass(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Age function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[mass_idx()]\n", - "\n", - "def halfmassrad_stars_idx():\n", - " return jnp.s_[:, 9]\n", - "\n", - "def halfmassrad_stars(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Halfmassrad_stars function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[halfmassrad_stars_idx()]\n", - "\n", - "\n", - "def redshift_idx():\n", - " return jnp.s_[:, 10]\n", - "\n", - "def redshift(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Redshift function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[redshift_idx()]\n", - "\n", - "def center_idx():\n", - " return jnp.s_[:, 11:14]\n", - "\n", - "def center(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Center function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[center_idx()]\n", - "\n", - "def mask_idx() :\n", - " return jnp.s_[:, 14]\n", - "\n", - "def mask(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Mask function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[mask_idx()]\n", - "\n", - "def pixel_assignment_idx() : \n", - " return jnp.s_[:, 15]\n", - "\n", - "def pixel_assignment(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Pixel assignment function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[pixel_assignment_idx()]\n", - "\n", - "\n", - "def spectra_index(): \n", - " return jnp.s_[:, 16:(16 + 5994)]\n", - "\n", - "def spectra(data: jnp.ndarray) -> jnp.ndarray:\n", - " \"\"\"\n", - " Spectra function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[spectra_index()]\n" - ] - }, - { - "cell_type": "markdown", - "id": "13", - "metadata": {}, - "source": [ - "try the sharding now with pipeline functions. since the pipeline functions use other data, I don´t use them directly, but build simplified versions here that only include stars. this involves the build up of the pipeline from the ground up in such a way that the data is sharded once and then we don´t have to touch it again" - ] - }, - { - "cell_type": "markdown", - "id": "14", - "metadata": {}, - "source": [ - "TODO: make sure the functions have the correct static argnums such that we don´t have to worry about the tracing shit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "from functools import partial\n", - "from pipe import Pipe\n", - "from rubix.galaxy.alignment import moment_of_inertia_tensor, rotation_matrix_from_inertia_tensor, apply_init_rotation, apply_rotation\n", - "from rubix.core.telescope import get_spatial_bin_edges\n", - "from rubix.telescope.utils import mask_particles_outside_aperture\n", - "from rubix.core.pipeline import RubixPipeline \n", - "from rubix.core.data import RubixData\n", - "from rubix.core.telescope import get_telescope\n", - "from jax import random as jrandom\n", - "from rubix.core.ssp import get_ssp, get_lookup_interpolation\n", - "from rubix.telescope.psf.kernels import gaussian_kernel_2d\n", - "from jax.scipy.signal import convolve2d\n", - "from rubix.telescope.lsf.lsf import _get_kernel\n", - "from jax.scipy.signal import convolve\n", - "from rubix import config as rubix_config" - ] - }, - { - "cell_type": "markdown", - "id": "16", - "metadata": {}, - "source": [ - "## galaxy rotation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "def rotate_galaxy_impl(data: jnp.array, alpha, beta, gamma)->jnp.array: \n", - "\n", - " I = moment_of_inertia_tensor(coords(data), mass(data), halfmassrad_stars(data),)\n", - " R = rotation_matrix_from_inertia_tensor(I)\n", - " data = data.at[coords_idx()].set(apply_rotation(apply_init_rotation(coords(data), R), alpha, beta, gamma))\n", - " data = data.at[velocity_idx()].set(apply_rotation(apply_init_rotation(velocity(data), R), alpha, beta, gamma))\n", - " return data\n", - "\n", - "# TODO: generalize, get these numbers from the config\n", - "rotate_galaxy = partial(rotate_galaxy_impl, alpha=90.0, beta=0.0, gamma=0.0)" - ] - }, - { - "cell_type": "markdown", - "id": "18", - "metadata": {}, - "source": [ - "## filter particles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# NBVAL_SKIP\n", - "\n", - "def filter_particles_impl(data: jnp.ndarray, spatial_bin_edges) -> jnp.ndarray:\n", - " mask = mask_particles_outside_aperture(\n", - " coords(data), spatial_bin_edges\n", - " )\n", - "\n", - " data = data.at[mask_idx()].set(mask)\n", - "\n", - " for attr in [age_idx, mass_idx, metallicity_idx, ]: \n", - " data = data.at[attr()].set(\n", - " jnp.where(mask, data[attr()], 0)\n", - " )\n", - "\n", - " return data\n", - "\n", - "filter_particles = partial(filter_particles_impl, spatial_bin_edges=get_spatial_bin_edges(config))" - ] - }, - { - "cell_type": "markdown", - "id": "20", - "metadata": {}, - "source": [ - "## spaxel assignment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "21", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "def spaxel_assignment_square_impl(data: jnp.ndarray, spatial_bin_edges)-> jnp.ndarray:\n", - " # Calculate assignment of of x and y coordinates to bins separately\n", - " x_indices = (\n", - " jnp.digitize(data[coords_idx()][:, 0], spatial_bin_edges) - 1\n", - " ) # -1 to start indexing at 0\n", - " y_indices = jnp.digitize(data[coords_idx()][:, 1], spatial_bin_edges) - 1\n", - "\n", - " number_of_bins = len(spatial_bin_edges) - 1\n", - "\n", - " # Clip the indices to the valid range\n", - " x_indices = jnp.clip(x_indices, 0, number_of_bins - 1)\n", - " y_indices = jnp.clip(y_indices, 0, number_of_bins - 1)\n", - "\n", - " # Flatten the 2D indices to 1D indices\n", - " pixel_positions = x_indices + (number_of_bins * y_indices)\n", - " return data.at[pixel_assignment_idx()].set(jnp.round(pixel_positions))\n", - "\n", - "\n", - "spaxel_assignment = partial(spaxel_assignment_square_impl, spatial_bin_edges=get_spatial_bin_edges(config))\n" - ] - }, - { - "cell_type": "markdown", - "id": "22", - "metadata": {}, - "source": [ - "## Calculate spectra" - ] - }, - { - "cell_type": "markdown", - "id": "23", - "metadata": {}, - "source": [ - "calculate spectra now. since this is so big, it would perpaps make sense to have a separate path for this thing instead of having to save this and drag it around all the time. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "# this needs to be optimized, it uses far too much memory\n", - "def calculate_spectra_impl(data: jnp.ndarray, lookup_interpolation) -> jnp.ndarray: \n", - " print(\"Calculating spectra\")\n", - " print(\"Data shape:\", data.shape)\n", - " print(\"lookup type: \", type(lookup_interpolation))\n", - " print(\"lookup shape: \", lookup_interpolation.shape)\n", - " # this thing is gigantic and probably cannot be stored in memory for serious data\n", - " return data.at[spectra_index()].set(lookup_interpolation(\n", - " data[metallicity_idx()],\n", - " data[age_idx()],\n", - " ))\n", - "# this creates a file access that should not be on the hot path. \n", - "lookup_interpolation = get_lookup_interpolation(config)\n", - "calculate_spectra = partial(calculate_spectra_impl, lookup_interpolation=lookup_interpolation)" - ] - }, - { - "cell_type": "markdown", - "id": "25", - "metadata": {}, - "source": [ - "## scale spectrum by mass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "def scale_spectrum_by_mass(data: jnp.ndarray) -> jnp.ndarray:\n", - "\n", - " return data.at[spectra_index()].set(\n", - " data[spectra_index()] * data[mass_idx()][:, jnp.newaxis]\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "27", - "metadata": {}, - "source": [ - "## doppler shift" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "# get all the needed crap... \n", - "velocity_direction = rubix_config[\"ifu\"][\"doppler\"][\"velocity_direction\"]\n", - "directions = {\"x\": 0, \"y\": 1, \"z\": 2}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "# TODO: this needs to be fused with the resampling step such that the giant temporary array is not created\n", - "def apply_doppler_impl(data: jnp.ndarray, wavelength, c, direction) -> jnp.ndarray:\n", - "\n", - " # 3 is the index of the first velocity component\n", - " d = jnp.exp(data[:, 3 + direction]/ c) # 3 is offset of the velocity component\n", - "\n", - " return jax.vmap(lambda d: wavelength * d)(d)\n", - "\n", - "ssp = get_ssp(config)\n", - "ssp_wave= ssp.wavelength\n", - "direction = directions[velocity_direction]\n", - "cosmological_doppler_shift = (1 + config[\"galaxy\"][\"dist_z\"]) * ssp.wavelength\n", - "\n", - "apply_doppler = partial(apply_doppler_impl, wavelength=ssp_wave, c=3e8, direction=direction)" - ] - }, - { - "cell_type": "markdown", - "id": "30", - "metadata": {}, - "source": [ - "## resampling" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def calculate_diff(\n", - " vec, pad_with_zero: bool = True\n", - "):\n", - " \"\"\"\n", - " Calculate the difference between each element in a vector.\n", - "\n", - " Args:\n", - " vec (array-like): The input vector.\n", - " pad_with_zero (bool, optional): Whether to prepend the first element of the vector to the differences. Default is True.\n", - "\n", - " Returns:\n", - " The differences between each element in the vector (array-like).\n", - " \"\"\"\n", - "\n", - " if pad_with_zero:\n", - " differences = jnp.diff(vec, prepend=vec[0])\n", - " else:\n", - " differences = jnp.diff(vec)\n", - " return differences\n", - "\n", - "\n", - "def resample_spectrum_impl(init_spectrum: jnp.ndarray, initial_wavelength, target_wavelength) -> jnp.ndarray:\n", - " in_range_mask = (initial_wavelength >= jnp.min(target_wavelength)) & (\n", - " initial_wavelength <= jnp.max(target_wavelength)\n", - " )\n", - "\n", - " intrinsic_wave_diff = calculate_diff(initial_wavelength) * in_range_mask\n", - "\n", - " # Get total luminsoity within the wavelength range\n", - " total_lum = jnp.sum(init_spectrum * intrinsic_wave_diff)\n", - "\n", - " # Interpolate the wavelegnth to the telescope grid\n", - " particle_lum = jnp.interp(target_wavelength, initial_wavelength, init_spectrum)\n", - "\n", - " # New total luminosity\n", - " new_total_lum = jnp.sum(particle_lum * calculate_diff(target_wavelength))\n", - "\n", - " # Factor to conserve flux in the new spectrum\n", - " scale_factor = total_lum / new_total_lum\n", - " scale_factor = jnp.nan_to_num(\n", - " scale_factor, nan=0.0\n", - " ) # Otherwise we get NaNs if new_total_lum is zero\n", - " lum = particle_lum * scale_factor\n", - "\n", - " return lum\n", - "\n", - "# indexing stuff for spectra\n", - "def rs_spectra_index(out_size: int): \n", - " return jnp.s_[:, 16:(16 + out_size)]\n", - "\n", - "def diff_spectra_index(in_size: int, out_size: int): \n", - " return jnp.s_[:, 16:(16 + (in_size - out_size))]\n", - "\n", - "def rs_spectra(data: jnp.ndarray, out_size: int) -> jnp.ndarray:\n", - " \"\"\"\n", - " Spectra function to be used in the pipeline.\n", - " \"\"\"\n", - " return data[rs_spectra_index(out_size)]\n", - "\n", - "def doppler_and_resample(data: jnp.array, target_wavelength: jnp.array, out_size: int) -> jnp.ndarray:\n", - " \"\"\"\n", - " Doppler shift and resample the spectrum.\n", - " \"\"\"\n", - " # Apply the doppler shift\n", - " v = apply_doppler(data)\n", - "\n", - " # Resample the spectrum\n", - " data = data.at[rs_spectra_index(out_size)].set(\n", - " jax.vmap(resample_spectrum_impl, in_axes=(0,0, None))(\n", - " data[spectra_index()], v, target_wavelength\n", - " )\n", - " )\n", - " data = data.at[diff_spectra_index(ssp_wave.shape[0], out_size)].set(0.0)\n", - "\n", - " return data\n", - "\n", - "telescope = get_telescope(config)\n", - "telescope_wavelength = telescope.wave_seq\n", - "num_spaxels = int(telescope.sbin)\n", - "out_size = int(telescope_wavelength.shape[0])\n", - "\n", - "resample = partial(doppler_and_resample,target_wavelength=telescope_wavelength, out_size = telescope_wavelength.shape[0])" - ] - }, - { - "cell_type": "markdown", - "id": "32", - "metadata": {}, - "source": [ - "get all the telescope data stuff and make a partial" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "33", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "telescope = get_telescope(config)\n", - "telescope_wavelength = telescope.wave_seq\n", - "num_spaxels = int(telescope.sbin)\n", - "out_size = int(telescope_wavelength.shape[0])\n", - "\n", - "resample = partial(doppler_and_resample,target_wavelength=telescope_wavelength, out_size = telescope_wavelength.shape[0])" - ] - }, - { - "cell_type": "markdown", - "id": "34", - "metadata": {}, - "source": [ - "## apply extinction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.telescope.utils import calculate_spatial_bin_edges\n", - "from rubix.core.cosmology import get_cosmology\n", - "from rubix.spectra.dust.extinction_models import Rv_model_dict, Cardelli89, Gordon23\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "galaxy_dist_z = config[\"galaxy\"][\"dist_z\"]\n", - "telescope = get_telescope(config)\n", - "telescope_wavelength = telescope.wave_seq\n", - "num_spaxels = int(telescope.sbin)\n", - "cosmology = get_cosmology(config)\n", - "ext_model = config[\"ssp\"][\"dust\"][\"extinction_model\"]\n", - "Rv = config[\"ssp\"][\"dust\"][\"Rv\"]\n", - "ext_model_class = Rv_model_dict[ext_model]\n", - "ext = ext_model_class(Rv=Rv)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "_, spatial_bin_size = calculate_spatial_bin_edges(fov =telescope.fov, spatial_bins = telescope.sbin, dist_z = galaxy_dist_z, cosmology = cosmology)\n", - "spaxel_area = spatial_bin_size**2\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "def apply_extinction(data: jnp.ndarray, wavelength, spaxel_area, n_spaxels, ext) -> jnp.ndarray:\n", - " # I don´t have gas in the data currently, so I skip this for now. \n", - " # The way it is done in the dust_extinction module has config lookups within the function, and the sorting should be avoided when possible! It's not clear why this is needed? \n", - " pass\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "39", - "metadata": {}, - "source": [ - "## calculate datacube" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def calculate_datacube_impl(data: jnp.ndarray, num_spaxels: int, out_size: int) -> jnp.ndarray:\n", - " return jax.ops.segment_sum(\n", - " data[rs_spectra_index(out_size)], # spectra\n", - " data[pixel_assignment_idx()].astype('int32'), # pixel assignment\n", - " num_segments=num_spaxels**2,\n", - " ).reshape(\n", - " (num_spaxels, num_spaxels, telescope_wavelength.shape[0])\n", - " )\n", - "\n", - "calculate_datacube = partial(calculate_datacube_impl, num_spaxels= int(telescope.sbin), out_size=out_size)" - ] - }, - { - "cell_type": "markdown", - "id": "41", - "metadata": {}, - "source": [ - "## convolve psf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "m, n = config[\"telescope\"][\"psf\"][\"size\"], config[\"telescope\"][\"psf\"][\"size\"]\n", - "sigma = config[\"telescope\"][\"psf\"][\"sigma\"]\n", - "kernel = gaussian_kernel_2d(m, n, sigma)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def apply_psf_impl(cube: jnp.ndarray, kernel) -> jnp.ndarray:\n", - "\n", - " return jnp.transpose(jax.vmap(partial(convolve2d, mode = \"same\"), in_axes = (2, None))(\n", - " cube, \n", - " kernel,\n", - " ), (1, 2, 0))\n", - "apply_psf = partial(apply_psf_impl, kernel=kernel)" - ] - }, - { - "cell_type": "markdown", - "id": "44", - "metadata": {}, - "source": [ - "## convolve lsf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "sigma = config[\"telescope\"][\"lsf\"][\"sigma\"]\n", - "telescope = get_telescope(config)\n", - "wave_resolution = telescope.wave_res\n", - "extend_factor = 12\n", - "\n", - "kernel = _get_kernel(sigma, wave_resolution, factor=extend_factor)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def apply_lsf_impl(cube: jnp.ndarray, kernel: jnp.array, extend_factor: int) -> jnp.ndarray:\n", - " reshaped_cube = cube.reshape(-1, cube.shape[-1])\n", - " convolved = jax.vmap(partial(convolve, mode=\"full\"), in_axes=(0, None))(reshaped_cube, kernel)\n", - " end = reshaped_cube.shape[1] + kernel.shape[0] - 1 - extend_factor\n", - " convolved= convolved[:, extend_factor:end]\n", - " return convolved.reshape(cube.shape)\n", - "\n", - "apply_lsf = partial(apply_lsf_impl, kernel=kernel, extend_factor=extend_factor)" - ] - }, - { - "cell_type": "markdown", - "id": "47", - "metadata": {}, - "source": [ - "## apply noise" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "signal_to_noise = config[\"telescope\"][\"noise\"][\"signal_to_noise\"]\n", - "\n", - "# Get the noise distribution\n", - "noise_distribution = config[\"telescope\"][\"noise\"][\"noise_distribution\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def calculate_S2N(cube: jnp.ndarray, observation_s2n: float)->jnp.ndarray: \n", - " flux_image = jnp.sum(cube, axis=2)\n", - " return jnp.where(flux_image > 0 , (jnp.sqrt(jnp.median(jnp.where(flux_image > 0 , flux_image, 0.)))/observation_s2n)/jnp.sqrt(flux_image), 0)\n", - "\n", - "def apply_noise_impl(cube: jnp.array, signal_to_noise: float) -> jnp.ndarray:\n", - " # TODO: this can probably be vmapped for better performance\n", - " key = jrandom.PRNGKey(0)\n", - " s2n = calculate_S2N(cube, signal_to_noise)\n", - " return cube + cube*jrandom.normal(key, cube.shape) * s2n[:, :, None] \n", - "\n", - "apply_noise = partial(apply_noise_impl, signal_to_noise=signal_to_noise)\n" - ] - }, - { - "cell_type": "markdown", - "id": "50", - "metadata": {}, - "source": [ - "## build pipelines" - ] - }, - { - "cell_type": "markdown", - "id": "51", - "metadata": {}, - "source": [ - "looks like everything is in place now, so we can build pipelines for the data transformations and the cube transformations. This is only done for sake of debugging, in production the separation is not needed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "52", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "@jax.jit\n", - "def transform_data(inputdata: jnp.ndarray) -> jnp.ndarray:\n", - "\n", - " data = rotate_galaxy(inputdata)\n", - " data = filter_particles(data)\n", - " data = spaxel_assignment(data)\n", - " data = calculate_spectra(data)\n", - " data = scale_spectrum_by_mass(data)\n", - " return data" - ] - }, - { - "cell_type": "markdown", - "id": "53", - "metadata": {}, - "source": [ - "this pipeline building and data prepare needs to go eventually" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "pipe = RubixPipeline(config)\n", - "inputdata = pipe.prepare_data()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = inputdata | Pipe(build_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "jax.debug.visualize_array_sharding(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = transform_data(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data.block_until_ready();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "data.shape, data.nbytes// 1024**2, data.nbytes/1024**3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "jax.debug.visualize_array_sharding(data)" - ] - }, - { - "cell_type": "markdown", - "id": "61", - "metadata": {}, - "source": [ - "The data array is still correctly sharded. yay!" - ] - }, - { - "cell_type": "markdown", - "id": "62", - "metadata": {}, - "source": [ - "when working with the cube pipeline now, we have to reshard it first and index into the padded cube or pad all the other data too. This is done in the `compute_cube` function using the first method" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "def reshard_cube(cube: jnp.ndarray,) -> jnp.ndarray:\n", - " d = cube.shape[2]\n", - "\n", - " # we can only go upwards to not loose\n", - " while d % jax.device_count() != 0:\n", - " d += 1\n", - " d\n", - " padding = d - cube.shape[2]\n", - " mesh = jax.make_mesh((jax.device_count(), ), ('devices',))\n", - " shard = NamedSharding(mesh, P(None, None, 'devices'))\n", - "\n", - " cube = jax.device_put(jnp.pad(cube, ((0, 0), (0, 0), (0, padding))), shard)\n", - " return cube\n", - "\n", - "def compute_cube(inputdata: jnp.ndarray) -> jnp.ndarray:\n", - " cube = calculate_datacube(inputdata)\n", - " \n", - " # not sure if this counteracts the sharding\n", - " cube = apply_psf(cube)\n", - " cube = apply_lsf(cube)\n", - " cube = apply_noise(cube)\n", - " return cube\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "64", - "metadata": {}, - "source": [ - "simple cube is not sharded" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "cube = calculate_datacube(data)\n", - "jax.debug.visualize_array_sharding(cube.reshape(cube.shape[0]* cube.shape[1], cube.shape[2]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "cube = reshard_cube(cube)\n", - "jax.debug.visualize_array_sharding(cube.reshape(cube.shape[0]* cube.shape[1], cube.shape[2]))" - ] - }, - { - "cell_type": "markdown", - "id": "67", - "metadata": {}, - "source": [ - "I have not applied this to the computation now because it is messy to do and it's not the main objective. this data cube is tiny by comparison. What one has to do is pad the data that takes part in the computations in the cube pipeline to the size of the cube. then the sharding should be fine. indexing into the cube will destroy the sharding again apparently, distributing it over all devices in the case of this tiny one. not good... " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "final_cube = compute_cube(data)\n", - "final_cube.block_until_ready()\n", - "jax.debug.visualize_array_sharding(final_cube.reshape(final_cube.shape[0]* final_cube.shape[1], final_cube.shape[2]))" - ] - }, - { - "cell_type": "markdown", - "id": "69", - "metadata": {}, - "source": [ - "not sharded correctly... :/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "\n", - "final_cube.shape, final_cube.nbytes / 1024**2, final_cube.dtype" - ] - }, - { - "cell_type": "markdown", - "id": "71", - "metadata": {}, - "source": [ - "... but it's also really small, so might be that? " - ] - }, - { - "cell_type": "markdown", - "id": "72", - "metadata": {}, - "source": [ - "## memory usage " - ] - }, - { - "cell_type": "markdown", - "id": "73", - "metadata": {}, - "source": [ - "The main point: which function causes memory explosion and why? " - ] - }, - { - "cell_type": "markdown", - "id": "74", - "metadata": {}, - "source": [ - "So far, we barely need 710 MB for the data cube, and we are not efficiently using memory at all. On multiple GPUs with overall O(100)GB, we should easily be able to process the required data sizes.\n", - "\n", - "**Expectation:**\n", - "For the 500k particles, this would amount to roughly (500/30)*710 = 11833, so 12 GB. Even with with double the number of spectral lines we should easily be able to run this on a 4090. up to ~800k particles on a single GPU with the current spectral line number should also be doable, and we do not talk about sharding here at all. \n", - "\n", - "When we have gas, this goes down by half. At any rate, how can this computation cause memory issues on this gpu?\n", - "\n", - "**Observation**\n", - "However, something temporarily causes a gigantic number of allocations in temporary arrays that lets memory usage go up to 40G or more. this is the killer element, I don't think that the sharding as such is a problem. \n", - "\n", - "Experiments above show that it's happening when processing the data itself, the cube computations are harmless." - ] - }, - { - "cell_type": "markdown", - "id": "75", - "metadata": {}, - "source": [ - "check each function of the pipeline with htop/nvtop or similar tools: htop -d 3 --> update ever 0.3 seconds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = build_data(inputdata)\n", - "data.block_until_ready(); # not the culprit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = rotate_galaxy(data)\n", - "data.block_until_ready(); #not the culprit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = filter_particles(data)\n", - "data.block_until_ready(); #not the culprit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = spaxel_assignment(data)\n", - "data.block_until_ready(); #not the culprit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = calculate_spectra(data)\n", - "data.block_until_ready(); # very much the culprit! increases memory size to > 40 GB even though the input is only ~0.7 - 0.8 GB" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = scale_spectrum_by_mass(data)\n", - "data.block_until_ready(); #not the culprit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "82", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "data = resample(data)\n", - "data.block_until_ready(); # moderate increase, not beyond a manageable size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "cube = calculate_datacube(data)\n", - "cube.block_until_ready(); #not the culprit" - ] - }, - { - "cell_type": "markdown", - "id": "84", - "metadata": {}, - "source": [ - "just to be sure: check cube computation agani" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "85", - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "final_cube = compute_cube(data)\n", - "final_cube.block_until_ready(); #not the culprit at all" - ] - }, - { - "cell_type": "markdown", - "id": "86", - "metadata": {}, - "source": [ - "There is a big problem in the spectra calculation that causes an enormous temporary memory issue. " - ] - }, - { - "cell_type": "markdown", - "id": "87", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb index a97e9671..2def84d5 100644 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ b/notebooks/rubix_pipeline_single_function_scaling.ipynb @@ -86,7 +86,7 @@ "import os\n", "\n", "config_TNG = {\n", - " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", " \"logger\": {\n", " \"log_level\": \"DEBUG\",\n", @@ -369,7 +369,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 1953c012..f4d2ca04 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -475,7 +475,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "rubix", "language": "python", "name": "python3" }, @@ -489,7 +489,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb index cc6411fa..19dd84a0 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb @@ -111,7 +111,7 @@ "galaxy_id = \"g7.66e11\"\n", "\n", "config_NIHAO = {\n", - " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", " \"logger\": {\n", " \"log_level\": \"DEBUG\",\n", @@ -174,7 +174,7 @@ "source": [ "# NBVAL_SKIP\n", "config_TNG = {\n", - " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", " \"logger\": {\n", " \"log_level\": \"DEBUG\",\n", @@ -520,7 +520,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "rubix", "language": "python", "name": "python3" }, @@ -534,7 +534,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb index a77b3062..ec20e063 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb @@ -114,7 +114,7 @@ "galaxy_id = \"g8.13e11\"\n", "\n", "config_NIHAO = {\n", - " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", " \"logger\": {\n", " \"log_level\": \"DEBUG\",\n", @@ -177,7 +177,7 @@ "source": [ "# NBVAL_SKIP\n", "config_TNG = {\n", - " \"pipeline\":{\"name\": \"calc_ifu_memory\"},\n", + " \"pipeline\":{\"name\": \"calc_ifu\"},\n", " \n", " \"logger\": {\n", " \"log_level\": \"DEBUG\",\n", @@ -495,7 +495,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "rubix", "language": "python", "name": "python3" }, @@ -509,7 +509,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/rubix/config/pipeline_config.yml b/rubix/config/pipeline_config.yml index 8f19bdcc..69b6e085 100644 --- a/rubix/config/pipeline_config.yml +++ b/rubix/config/pipeline_config.yml @@ -15,31 +15,14 @@ calc_ifu: depends_on: filter_particles args: [] kwargs: {} - - calculate_spectra: - name: calculate_spectra + calculate_datacube_particlewise: + name: calculate_datacube_particlewise depends_on: spaxel_assignment args: [] kwargs: {} - - scale_spectrum_by_mass: - name: scale_spectrum_by_mass - depends_on: calculate_spectra - args: [] - kwargs: {} - doppler_shift_and_resampling: - name: doppler_shift_and_resampling - depends_on: scale_spectrum_by_mass - args: [] - kwargs: {} - calculate_datacube: - name: calculate_datacube - depends_on: doppler_shift_and_resampling - args: [] - kwargs: {} convolve_psf: name: convolve_psf - depends_on: calculate_datacube + depends_on: calculate_datacube_particlewise args: [] kwargs: {} convolve_lsf: @@ -53,7 +36,7 @@ calc_ifu: args: [] kwargs: {} -calc_ifu_memory: +calc_dusty_ifu: Transformers: rotate_galaxy: name: rotate_galaxy @@ -70,14 +53,14 @@ calc_ifu_memory: depends_on: filter_particles args: [] kwargs: {} - calculate_datacube_particlewise: - name: calculate_datacube_particlewise + calculate_dusty_datacube_particlewise: + name: calculate_dusty_datacube_particlewise depends_on: spaxel_assignment args: [] kwargs: {} convolve_psf: name: convolve_psf - depends_on: calculate_datacube_particlewise + depends_on: calculate_dusty_datacube_particlewise args: [] kwargs: {} convolve_lsf: @@ -91,59 +74,26 @@ calc_ifu_memory: args: [] kwargs: {} -calc_dusty_ifu: +calc_gradient: Transformers: rotate_galaxy: name: rotate_galaxy depends_on: null args: [] kwargs: {} - filter_particles: - name: filter_particles - depends_on: rotate_galaxy - args: [] - kwargs: {} spaxel_assignment: name: spaxel_assignment - depends_on: filter_particles + depends_on: rotate_galaxy args: [] kwargs: {} - - reshape_data: - name: reshape_data + calculate_datacube_particlewise: + name: calculate_datacube_particlewise depends_on: spaxel_assignment args: [] kwargs: {} - - calculate_spectra: - name: calculate_spectra - depends_on: reshape_data - args: [] - kwargs: {} - - scale_spectrum_by_mass: - name: scale_spectrum_by_mass - depends_on: calculate_spectra - args: [] - kwargs: {} - doppler_shift_and_resampling: - name: doppler_shift_and_resampling - depends_on: scale_spectrum_by_mass - args: [] - kwargs: {} - calculate_extinction: - name: calculate_extinction - depends_on: doppler_shift_and_resampling - args: [] - kwargs: {} - calculate_datacube: - name: calculate_datacube - depends_on: calculate_extinction - args: [] - kwargs: {} convolve_psf: name: convolve_psf - depends_on: calculate_datacube + depends_on: calculate_datacube_particlewise args: [] kwargs: {} convolve_lsf: @@ -155,4 +105,4 @@ calc_dusty_ifu: name: apply_noise depends_on: convolve_lsf args: [] - kwargs: {} + kwargs: {} \ No newline at end of file From 35061109c0775008fb2d004aaa64f9c7c42186ed Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 4 Jul 2025 10:31:38 +0000 Subject: [PATCH 64/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rubix/config/pipeline_config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rubix/config/pipeline_config.yml b/rubix/config/pipeline_config.yml index 69b6e085..477b1fdc 100644 --- a/rubix/config/pipeline_config.yml +++ b/rubix/config/pipeline_config.yml @@ -105,4 +105,4 @@ calc_gradient: name: apply_noise depends_on: convolve_lsf args: [] - kwargs: {} \ No newline at end of file + kwargs: {} From 844c902fba28177d81f351c460d5ba048ea0f3ba Mon Sep 17 00:00:00 2001 From: anschaible Date: Fri, 4 Jul 2025 14:08:22 +0200 Subject: [PATCH 65/76] remove outdated stuff --- rubix/core/data.py | 95 +-------- rubix/core/ifu.py | 296 -------------------------- rubix/core/pipeline.py | 55 ----- rubix/galaxy/input_handler/pynbody.py | 13 +- tests/test_core_ifu.py | 1 - 5 files changed, 2 insertions(+), 458 deletions(-) diff --git a/rubix/core/data.py b/rubix/core/data.py index dea16588..3587f568 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -15,51 +15,6 @@ from rubix.logger import get_logger from rubix.utils import load_galaxy_data, read_yaml -# class Particles: -# def __init__(self, particle_data: object): -# self.particle_data = particle_data -# self.attributes = self._filter_attributes() -# -# def _filter_attributes(self) -> list: -# """ -# Filters the attributes of the particle_data object based on the specified criteria. -# """ -# return [ -# attr -# for attr in dir(self.particle_data) -# if not attr.startswith("__") -# and not callable(getattr(self.particle_data, attr)) -# ] -# -# def get_attributes(self) -> list: -# """ -# Returns the filtered attributes. -# """ -# return self.attributes - - -# class Particles: -# def __init__(self, particle_data: object): -# self.particle_data = particle_data -# self.attributes = self._filter_attributes() -# -# def _filter_attributes(self) -> list: -# """ -# Filters the attributes of the particle_data object based on the specified criteria. -# """ -# return [ -# attr -# for attr in dir(self.particle_data) -# if not attr.startswith("__") -# and not callable(getattr(self.particle_data, attr)) -# ] -# -# def get_attributes(self) -> list: -# """ -# Returns the filtered attributes. -# """ -# return self.attributes - # Registering the dataclass with JAX for automatic tree traversal # @jaxtyped(typechecker=typechecker) @@ -79,19 +34,6 @@ class Galaxy: center: Optional[jnp.ndarray] = None halfmassrad_stars: Optional[jnp.ndarray] = None - # def __repr__(self): - # representationString = ["Galaxy:"] - # for k, v in self.__dict__.items(): - # if not k.endswith("_unit"): - # if v is not None: - # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - # attrString += f", unit = {getattr(self, k + '_unit')}" - # representationString.append(attrString) - # else: - # representationString.append(f"{k}: None") - # return "\n\t".join(representationString) - def tree_flatten(self): """ Flattens the Galaxy object into a tuple of children and auxiliary data @@ -152,18 +94,6 @@ class StarsData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - # def __repr__(self): - # representationString = ["StarsData:"] - # for k, v in self.__dict__.items(): - # if not k.endswith("_unit"): - # if v is not None: - # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - # attrString += f", unit = {getattr(self, k + '_unit')}" - # representationString.append(attrString) - # else: - # representationString.append(f"{k}: None") - # return "\n\t".join(representationString) def tree_flatten(self): """ @@ -242,19 +172,7 @@ class GasData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - # def __repr__(self): - # representationString = ["GasData:"] - # for k, v in self.__dict__.items(): - # if not k.endswith("_unit"): - # if v is not None: - # attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" - # if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": - # attrString += f", unit = {getattr(self, k + '_unit')}" - # representationString.append(attrString) - # else: - # representationString.append(f"{k}: None") - # return "\n\t".join(representationString) - + def tree_flatten(self): """ Flattens the Gas object into a tuple of children and auxiliary data @@ -315,17 +233,6 @@ class RubixData: stars: Optional[StarsData] = None gas: Optional[GasData] = None - # def __repr__(self): - # representationString = ["RubixData:"] - # for k, v in self.__dict__.items(): - # representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) - # return "\n\t".join(representationString) - - # def __post_init__(self): - # if self.stars is not None: - # self.stars = Particles(self.stars) - # if self.gas is not None: - # self.gas = Particles(self.gas) def tree_flatten(self): """ diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 86b60e91..5d76e64f 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -27,302 +27,6 @@ from .telescope import get_telescope -@jaxtyped(typechecker=typechecker) -def get_calculate_spectra(config: dict) -> Callable: - """ - This function is outdated, we do not recommend using it for a large set of particles! - We recommend using the function get_calculate_datacube_particlewise! - The function gets the lookup function that performs the lookup to the SSP model, - and parallelizes the funciton across all GPUs. - - Args: - config (dict): The configuration dictionary - - Returns: - The function that calculates the spectra of the stars. - - Example - ------- - >>> config = { - ... "ssp": { - ... "template": { - ... "name": "BruzualCharlot2003" - ... }, - ... }, - ... } - - >>> from rubix.core.ifu import get_calculate_spectra - >>> calcultae_spectra = get_calculate_spectra(config) - - >>> rubixdata = calcultae_spectra(rubixdata) - >>> # Access the spectra of the stars - >>> rubixdata.stars.spectra - """ - logger = get_logger(config.get("logger", None)) - # lookup_interpolation_pmap = get_lookup_interpolation_pmap(config) - # lookup_interpolation_vmap = get_lookup_interpolation_vmap(config) - lookup_interpolation = get_lookup_interpolation(config) - - def lookup_interpolation_laxmap(age_metallicity): - age, metallicity = age_metallicity - return lookup_interpolation(metallicity, age) - - @jaxtyped(typechecker=typechecker) - def calculate_spectra(rubixdata: RubixData) -> RubixData: - logger.info("Calculating IFU cube...") - logger.debug( - f"Input shapes: Metallicity: {len(rubixdata.stars.metallicity)}, Age: {len(rubixdata.stars.age)}" - ) - # Ensure metallicity and age are arrays and reshape them to be at least 1-dimensional - # age_data = jax.device_get(rubixdata.stars.age) - age_data = rubixdata.stars.age - # metallicity_data = jax.device_get(rubixdata.stars.metallicity) - metallicity_data = rubixdata.stars.metallicity - # Ensure they are not scalars or empty; convert to 1D arrays if necessary - age = jnp.atleast_1d(age_data) - metallicity = jnp.atleast_1d(metallicity_data) - - spectra = lookup_interpolation( - metallicity, - age, - ) - - logger.debug(f"Calculation Finished! Spectra shape: {spectra.shape}") - spectra_jax = jnp.array(spectra) - # spectra_jax = jnp.expand_dims(spectra_jax, axis=0) - rubixdata.stars.spectra = spectra_jax - # setattr(rubixdata.gas, "spectra", spectra) - # jax.debug.print("Calculate Spectra: Spectra {}", spectra) - return rubixdata - - return calculate_spectra - - -@jaxtyped(typechecker=typechecker) -def get_scale_spectrum_by_mass(config: dict) -> Callable: - """ - This function is outdates, we do not recomend to use it for a large set of particles! - We recommend to use the function get_calculate_datacube_particlewise! - The spectra of the stellar particles are scaled by the mass of the stars. - - Args: - config (dict): The configuration dictionary - Returns: - The function that scales the spectra by the mass of the stars. - - Example - ------- - >>> from rubix.core.ifu import get_scale_spectrum_by_mass - >>> scale_spectrum_by_mass = get_scale_spectrum_by_mass(config) - - >>> rubixdata = scale_spectrum_by_mass(rubixdata) - >>> # Access the spectra of the stars, which is now scaled by the stellar mass - >>> rubixdata.stars.spectra - """ - - logger = get_logger(config.get("logger", None)) - - @jaxtyped(typechecker=typechecker) - def scale_spectrum_by_mass(rubixdata: RubixData) -> RubixData: - - logger.info("Scaling Spectra by Mass...") - mass = jnp.expand_dims(rubixdata.stars.mass, axis=-1) - # rubixdata.stars.spectra = rubixdata.stars.spectra * mass - spectra_mass = rubixdata.stars.spectra * mass - setattr(rubixdata.stars, "spectra", spectra_mass) - # jax.debug.print("mass mult: Spectra {}", inputs["spectra"]) - return rubixdata - - return scale_spectrum_by_mass - - -# Vectorize the resample_spectrum function -@jaxtyped(typechecker=typechecker) -def get_resample_spectrum_vmap(target_wavelength) -> Callable: - """ - This function is outdates, we do not recomend to use it for a large set of particles! - We recommend to use the function get_calculate_datacube_particlewise! - The spectra of the stars are resampled to the telescope wavelength grid. - - Args: - target_wavelength (jax.Array): The telescope wavelength grid - - Returns: - The function that resamples the spectra to the telescope wavelength grid. - """ - - @jaxtyped(typechecker=typechecker) - def resample_spectrum_vmap(initial_spectrum, initial_wavelength): - return resample_spectrum( - initial_spectrum=initial_spectrum, - initial_wavelength=initial_wavelength, - target_wavelength=target_wavelength, - ) - - return jax.vmap(resample_spectrum_vmap, in_axes=(0, 0)) - - -@jaxtyped(typechecker=typechecker) -def get_velocities_doppler_shift_vmap( - ssp_wave: Float[Array, "..."], velocity_direction: str -) -> Callable: - """ - This function is outdates, we do not recomend to use it for a large set of particles! - We recommend to use teh function get_calculate_datacube_particlewise! - The function doppler shifts the wavelength based on the velocity of the stars. - - Args: - ssp_wave (jax.Array): The wavelength of the SSP grid - velocity_direction (str): The velocity component of the stars that is used to doppler shift the wavelength - - Returns: - The function that doppler shifts the wavelength based on the velocity of the stars. - """ - - # def func(velocity): - # return velocity_doppler_shift( - # wavelength=ssp_wave, velocity=velocity, direction=velocity_direction - # ) - - # return jax.vmap(func, in_axes=0) - def doppler_fn(velocities): - return velocity_doppler_shift( - wavelength=ssp_wave, - velocity=velocities, - direction=velocity_direction, - ) - - return doppler_fn - - -@jaxtyped(typechecker=typechecker) -def get_doppler_shift_and_resampling(config: dict) -> Callable: - """ - This function is outdates, we do not recomend to use it for a large set of particles! - We recommend to use the function get_calculate_datacube_particlewise! - The function doppler shifts the wavelength based on the velocity of the stars and resamples the spectra to the telescope wavelength grid. - - Args: - config (dict): The configuration dictionary - - Returns: - The function that doppler shifts the wavelength based on the velocity of the stars and resamples the spectra to the telescope wavelength grid. - - Example - ------- - >>> from rubix.core.ifu import get_doppler_shift_and_resampling - >>> doppler_shift_and_resampling = get_doppler_shift_and_resampling(config) - - >>> rubixdata = doppler_shift_and_resampling(rubixdata) - >>> # Access the spectra of the stars, which is now doppler shifted and resampled to the telescope wavelength grid - >>> rubixdata.stars.spectra - """ - logger = get_logger(config.get("logger", None)) - - # The velocity component of the stars that is used to doppler shift the wavelength - velocity_direction = rubix_config["ifu"]["doppler"]["velocity_direction"] - - # The redshift at which the user wants to observe the galaxy - galaxy_redshift = config["galaxy"]["dist_z"] - - # Get the telescope wavelength bins - telescope = get_telescope(config) - telescope_wavelength = telescope.wave_seq - - # Get the SSP grid to doppler shift the wavelengths - ssp = get_ssp(config) - - # Doppler shift the SSP wavelenght based on the cosmological distance of the observed galaxy - ssp_wave = cosmological_doppler_shift(z=galaxy_redshift, wavelength=ssp.wavelength) - logger.debug(f"SSP Wave: {ssp_wave.shape}") - - # Function to Doppler shift the wavelength based on the velocity of the stars particles - # This binds the velocity direction, such that later we only need the velocity during the pipeline - doppler_shift = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction) - - @jaxtyped(typechecker=typechecker) - def process_particle( - particle: Union[StarsData, GasData], - ) -> Union[Float[Array, "..."], None]: - if particle.spectra is not None: - # Doppler shift based on the velocity of the particle - doppler_shifted_ssp_wave = doppler_shift(particle.velocity) - logger.info(f"Doppler shifting and resampling spectra...") - logger.debug(f"Doppler Shifted SSP Wave: {doppler_shifted_ssp_wave.shape}") - logger.debug(f"Telescope Wave Seq: {telescope_wavelength.shape}") - - # Function to resample the spectrum to the telescope wavelength grid - # resample_spectrum_pmap = get_resample_spectrum_pmap(telescope_wavelength) - # spectrum_resampled = resample_spectrum_pmap( - # particle.spectra, doppler_shifted_ssp_wave - # ) - resample_fn = get_resample_spectrum_vmap(telescope_wavelength) - spectrum_resampled = resample_fn(particle.spectra, doppler_shifted_ssp_wave) - return spectrum_resampled - return particle.spectra - - @jaxtyped(typechecker=typechecker) - def doppler_shift_and_resampling(rubixdata: RubixData) -> RubixData: - for particle_name in ["stars", "gas"]: - particle = getattr(rubixdata, particle_name) - particle.spectra = process_particle(particle) - - return rubixdata - - return doppler_shift_and_resampling - - -@jaxtyped(typechecker=typechecker) -def get_calculate_datacube(config: dict) -> Callable: - """ - This function is outdates, we do not recomend to use it for a large set of particles! - We recommend to use the function get_calculate_datacube_particlewise! - The function returns the function that calculates the datacube of the stars. - - Args: - config (dict): The configuration dictionary - - Returns: - The function that calculates the datacube of the stars. - - Example - ------- - >>> from rubix.core.ifu import get_calculate_datacube - >>> calculate_datacube = get_calculate_datacube(config) - - >>> rubixdata = calculate_datacube(rubixdata) - >>> # Access the datacube of the stars - >>> rubixdata.stars.datacube - """ - logger = get_logger(config.get("logger", None)) - telescope = get_telescope(config) - num_spaxels = int(telescope.sbin) - - # Bind the num_spaxels to the function - # calculate_cube_fn = jax.tree_util.Partial(calculate_cube, num_spaxels=num_spaxels) - # calculate_cube_pmap = jax.pmap(calculate_cube_fn) - - @jaxtyped(typechecker=typechecker) - def calculate_datacube(rubixdata: RubixData) -> RubixData: - logger.info("Calculating Data Cube...") - # ifu_cubes = calculate_cube_fn( - # spectra=rubixdata.stars.spectra, - # spaxel_index=rubixdata.stars.pixel_assignment, - # ) - datacube = calculate_cube( - rubixdata.stars.spectra, rubixdata.stars.pixel_assignment, num_spaxels - ) - # datacube = jnp.sum(ifu_cubes, axis=0) - logger.debug(f"Datacube Shape: {datacube.shape}") - # logger.debug(f"This is the datacube: {datacube}") - datacube_jax = jnp.array(datacube) - setattr(rubixdata.stars, "datacube", datacube_jax) - # rubixdata.stars.datacube = datacube - return rubixdata - - return calculate_datacube - - @jaxtyped(typechecker=typechecker) def get_calculate_datacube_particlewise(config: dict) -> Callable: """ diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 94f96573..f385f898 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -135,61 +135,6 @@ def _get_pipeline_functions(self) -> list: ] return functions - def run(self, inputdata): - """ - Runs the data processing pipeline on the complete input data. - - Parameters - ---------- - inputdata : object - Data prepared from the `prepare_data` method. - - Returns - ------- - object - Pipeline output (which includes the datacube and unit attributes). - """ - time_start = time.time() - functions = self._get_pipeline_functions() - self._pipeline = pipeline.LinearTransformerPipeline( - self.pipeline_config, functions - ) - self.logger.info("Assembling the pipeline...") - self._pipeline.assemble() - self.logger.info("Compiling the expressions...") - self.func = self._pipeline.compile_expression() - self.logger.info("Running the pipeline on the input data...") - output = self.func(inputdata) - block_until_ready(output) - time_end = time.time() - self.logger.info( - "Pipeline run completed in %.2f seconds.", time_end - time_start - ) - - """ - # Propagate unit attributes from input to output. - output.galaxy.redshift_unit = inputdata.galaxy.redshift_unit - output.galaxy.center_unit = inputdata.galaxy.center_unit - output.galaxy.halfmassrad_stars_unit = inputdata.galaxy.halfmassrad_stars_unit - - if output.stars.coords is not None: - output.stars.coords_unit = inputdata.stars.coords_unit - output.stars.velocity_unit = inputdata.stars.velocity_unit - output.stars.mass_unit = inputdata.stars.mass_unit - output.stars.age_unit = inputdata.stars.age_unit - output.stars.spatial_bin_edges_unit = "kpc" - - if output.gas.coords is not None: - output.gas.coords_unit = inputdata.gas.coords_unit - output.gas.velocity_unit = inputdata.gas.velocity_unit - output.gas.mass_unit = inputdata.gas.mass_unit - output.gas.density_unit = inputdata.gas.density_unit - output.gas.internal_energy_unit = inputdata.gas.internal_energy_unit - output.gas.sfr_unit = inputdata.gas.sfr_unit - output.gas.electron_abundance_unit = inputdata.gas.electron_abundance_unit - output.gas.spatial_bin_edges_unit = "kpc" - """ - return output def run_sharded(self, inputdata): """ diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index 8df9f1e3..fcae628c 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -98,16 +98,6 @@ def load_data(self): getattr(self.sim, cls), fields[cls], units[cls], cls ) - # for cls in self.data: - # self.logger.info(f"Loaded {cls} data: {self.data[cls].keys()}") - # self.logger.info("Assigning metals to gas particles........") - - # Combine HI and OxMassFrac into a two-column metals field for gas - # self.data["gas"]["metals"] = np.column_stack((self.data["gas"]["HI"], - # self.data["gas"]["OxMassFrac"])) - # self.logger.info("Metals assigned to gas particles........") - # self.logger.info("Metals shape is: ", self.data["gas"]["metals"].shape) - hi_data = self.load_particle_data( getattr(self.sim, "gas"), {"HI": "HI"}, @@ -120,8 +110,7 @@ def load_data(self): {"OxMassFrac": u.dimensionless_unscaled}, "gas", ) - # fe_data = self.load_particle_data(getattr(self.sim, "gas"), {"FeMassFrac": "FeMassFrac"}, {"FeMassFrac": u.dimensionless_unscaled}, "gas") - # self.data["gas"]["metals"] = np.column_stack((hi_data["HI"], ox_data["OxMassFrac"])) + # Create a metals array with 10 columns, filled with zeros initially n_particles = hi_data["HI"].shape[0] metals = np.zeros((n_particles, 10), dtype=hi_data["HI"].dtype) diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index 03c6b841..e1e3fd20 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -32,7 +32,6 @@ print("Sample_inputs:") for key in sample_inputs: - # sample_inputs[key] = reshape_array(sample_inputs[key]) print(f"Key: {key}, shape: {sample_inputs[key].shape}") From 91941fff02bbf741ea9b8c16b07a8519f487b6af Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 4 Jul 2025 12:08:41 +0000 Subject: [PATCH 66/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rubix/core/data.py | 3 --- rubix/core/pipeline.py | 1 - 2 files changed, 4 deletions(-) diff --git a/rubix/core/data.py b/rubix/core/data.py index 3587f568..361c96f4 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -94,7 +94,6 @@ class StarsData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - def tree_flatten(self): """ Flattens the Stars object into a tuple of children and auxiliary data @@ -172,7 +171,6 @@ class GasData: spectra: Optional[jnp.ndarray] = None datacube: Optional[jnp.ndarray] = None - def tree_flatten(self): """ Flattens the Gas object into a tuple of children and auxiliary data @@ -233,7 +231,6 @@ class RubixData: stars: Optional[StarsData] = None gas: Optional[GasData] = None - def tree_flatten(self): """ Flattens the RubixData object into a tuple of children and auxiliary data diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index f385f898..b58856cf 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -135,7 +135,6 @@ def _get_pipeline_functions(self) -> list: ] return functions - def run_sharded(self, inputdata): """ Runs the pipeline on sharded input data in parallel using jax.shard_map. From 9dda882739aec5694fa891ff5e177c325fd8d3e8 Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Wed, 9 Jul 2025 11:32:25 +0200 Subject: [PATCH 67/76] added padding function --- rubix/core/pipeline.py | 20 ++++++-------------- rubix/utils.py | 17 +++++++++++++++++ 2 files changed, 23 insertions(+), 14 deletions(-) diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index b58856cf..44098611 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -19,7 +19,7 @@ from rubix.logger import get_logger from rubix.pipeline import linear_pipeline as pipeline -from rubix.utils import get_config, get_pipeline_config +from rubix.utils import _pad_particles, get_config, get_pipeline_config from .data import ( Galaxy, @@ -229,23 +229,15 @@ def run_sharded(self, inputdata): lambda s: s.spec if isinstance(s, NamedSharding) else None, rubix_spec ) - # if the particle number is not modulo the device number, we have to padd a few empty particles + # if the particle number is not modulo the device number, we have to pad a few empty particles # to make it work - # this is a bit of a hack, but it works - n = inputdata.stars.coords.shape[0] pad = (num_devices - (n % num_devices)) % num_devices - if pad: - # pad along the first axis - inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0, pad), (0, 0))) - inputdata.stars.velocity = jnp.pad( - inputdata.stars.velocity, ((0, pad), (0, 0)) - ) - inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0, pad))) - inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0, pad))) - inputdata.stars.metallicity = jnp.pad( - inputdata.stars.metallicity, ((0, pad)) + self.logger.info( + "Padding particles to make the number of particles divisible by the number of devices (%d).", + num_devices, ) + inputdata = _pad_particles(inputdata, pad) inputdata = jax.device_put(inputdata, rubix_spec) diff --git a/rubix/utils.py b/rubix/utils.py index 07cf77d7..26e7d308 100644 --- a/rubix/utils.py +++ b/rubix/utils.py @@ -3,6 +3,7 @@ from typing import Dict, Union import h5py +import jax.numpy as jnp import yaml from astropy.cosmology import Planck15 as cosmo @@ -195,3 +196,19 @@ def load_galaxy_data(path_to_file: str): units[key][field] = f[f"particles/{key}/{field}"].attrs["unit"] return galaxy_data, units + + +def _pad_particles(inputdata, pad: int) -> "InputData": + """ + Pads the particle arrays in inputdata to make their length divisible by num_devices. + This is necessary for sharding to work correctly. + """ + + # pad along the first axis + inputdata.stars.coords = jnp.pad(inputdata.stars.coords, ((0, pad), (0, 0))) + inputdata.stars.velocity = jnp.pad(inputdata.stars.velocity, ((0, pad), (0, 0))) + inputdata.stars.mass = jnp.pad(inputdata.stars.mass, ((0, pad))) + inputdata.stars.age = jnp.pad(inputdata.stars.age, ((0, pad))) + inputdata.stars.metallicity = jnp.pad(inputdata.stars.metallicity, ((0, pad))) + + return inputdata From d2f0d1594de5f23a8a6f479779cb746a3f9db66d Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Wed, 9 Jul 2025 11:48:43 +0200 Subject: [PATCH 68/76] fix tests of ifu pipelin --- rubix/config/pynbody_config.yml | 1 - rubix/core/pipeline.py | 6 ++-- tests/test_core_ifu.py | 55 --------------------------------- 3 files changed, 3 insertions(+), 59 deletions(-) diff --git a/rubix/config/pynbody_config.yml b/rubix/config/pynbody_config.yml index d25f0459..802dc9ef 100644 --- a/rubix/config/pynbody_config.yml +++ b/rubix/config/pynbody_config.yml @@ -34,7 +34,6 @@ units: metals: "dimensionless" #OxMassFrac: "dimensionless" #HI: "dimensionless" - metallicity: "Zsun" coords: "kpc" velocity: "km/s" mass: "Msun" diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 44098611..55ac0c31 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -31,7 +31,6 @@ ) from .dust import get_extinction from .ifu import ( - get_calculate_datacube, get_calculate_datacube_particlewise, get_calculate_spectra, get_doppler_shift_and_resampling, @@ -177,6 +176,7 @@ def run_sharded(self, inputdata): replicate_1d = NamedSharding(mesh, P(None)) # for 1-D arrays shard_2d = NamedSharding(mesh, P("data", None)) # for (N, D) shard_1d = NamedSharding(mesh, P("data")) # for (N,) + shard_bins = NamedSharding(mesh, P(None, None)) replicate_3d = NamedSharding(mesh, P(None, None, None)) # for full cube # — 1) allocate empty instances — @@ -198,7 +198,7 @@ def run_sharded(self, inputdata): stars_spec.age = shard_1d stars_spec.metallicity = shard_1d stars_spec.pixel_assignment = shard_1d - stars_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + stars_spec.spatial_bin_edges = shard_bins stars_spec.mask = shard_1d stars_spec.spectra = shard_2d stars_spec.datacube = replicate_3d @@ -214,7 +214,7 @@ def run_sharded(self, inputdata): gas_spec.sfr = shard_1d gas_spec.electron_abundance = shard_1d gas_spec.pixel_assignment = shard_1d - gas_spec.spatial_bin_edges = NamedSharding(mesh, P(None, None)) + gas_spec.spatial_bin_edges = shard_bins gas_spec.mask = shard_1d gas_spec.spectra = shard_2d gas_spec.datacube = replicate_3d diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index e1e3fd20..e54ba5a2 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -4,7 +4,6 @@ from rubix.core.data import Galaxy, GasData, RubixData, StarsData, reshape_array from rubix.core.ifu import ( - get_calculate_datacube, get_calculate_datacube_particlewise, get_calculate_spectra, get_doppler_shift_and_resampling, @@ -309,60 +308,6 @@ def test_doppler_shift_and_resampling(): ), "NaN values found in result spectra" -def test_get_calculate_datacube(): - # Setup: Telescope from config - config = { - "pipeline": {"name": "calc_ifu"}, - "logger": { - "log_level": "DEBUG", - "log_file_path": None, - "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", - }, - "telescope": {"name": "MUSE"}, - "cosmology": {"name": "PLANCK15"}, - "galaxy": {"dist_z": 0.1}, - "ssp": {"template": {"name": "BruzualCharlot2003"}}, - } - telescope = get_telescope(config) - n_spaxels = int(telescope.sbin) - n_wave = telescope.wave_seq.shape[0] - n_particles = 3 - - # Make spectra: shape (n_particles, n_wave) - spectra = jnp.arange(n_particles * n_wave, dtype=jnp.float32).reshape( - n_particles, n_wave - ) - - # Assign each particle to a spaxel - pixel_assignment = jnp.array([0, 1, n_spaxels**2 - 1], dtype=jnp.int32) - - # Build stars data - stars = StarsData() - stars.spectra = spectra - stars.pixel_assignment = pixel_assignment - - # Build rubixdata - rubixdata = RubixData(galaxy=Galaxy(), stars=stars, gas=GasData()) - - # Run pipeline - calculate_datacube = get_calculate_datacube(config) - result = calculate_datacube(rubixdata) - - # Check datacube: shape (n_spaxels, n_spaxels, n_wave) - assert hasattr(result.stars, "datacube") - assert result.stars.datacube.shape == (n_spaxels, n_spaxels, n_wave) - - # Check that each pixel has the correct sum of spectra (simple case: only one particle per spaxel) - flat_cube = result.stars.datacube.reshape(-1, n_wave) - for i, pix in enumerate(pixel_assignment): - assert jnp.allclose(flat_cube[pix], spectra[i]) - - # All other spaxels should be zero - mask = jnp.ones((n_spaxels**2,), dtype=bool) - mask = mask.at[pixel_assignment].set(False) - assert jnp.all(flat_cube[mask] == 0) - - def test_get_calculate_datacube_particlewise(): # Setup config and telescope config = { From 34e558e50dde11460ad0e3f2a79904181384769a Mon Sep 17 00:00:00 2001 From: Tobias Buck Date: Thu, 10 Jul 2025 09:24:52 +0200 Subject: [PATCH 69/76] fix failing pytests --- rubix/core/ifu.py | 9 +- rubix/core/pipeline.py | 18 +-- tests/test_core_ifu.py | 244 +----------------------------------- tests/test_core_pipeline.py | 102 +-------------- 4 files changed, 6 insertions(+), 367 deletions(-) diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 5d76e64f..4f52b3cb 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -11,19 +11,12 @@ from rubix.logger import get_logger from rubix.spectra.ifu import ( _velocity_doppler_shift_single, - calculate_cube, cosmological_doppler_shift, resample_spectrum, - velocity_doppler_shift, ) from .data import RubixData -from .ssp import ( - get_lookup_interpolation, - get_lookup_interpolation_pmap, - get_lookup_interpolation_vmap, - get_ssp, -) +from .ssp import get_lookup_interpolation, get_ssp from .telescope import get_telescope diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 55ac0c31..9e9e0db4 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -30,12 +30,7 @@ get_rubix_data, ) from .dust import get_extinction -from .ifu import ( - get_calculate_datacube_particlewise, - get_calculate_spectra, - get_doppler_shift_and_resampling, - get_scale_spectrum_by_mass, -) +from .ifu import get_calculate_datacube_particlewise from .lsf import get_convolve_lsf from .noise import get_apply_noise from .psf import get_convolve_psf @@ -102,14 +97,8 @@ def _get_pipeline_functions(self) -> list: rotate_galaxy = get_galaxy_rotation(self.user_config) filter_particles = get_filter_particles(self.user_config) spaxel_assignment = get_spaxel_assignment(self.user_config) - calculate_spectra = get_calculate_spectra(self.user_config) # reshape_data = get_reshape_data(self.user_config) - scale_spectrum_by_mass = get_scale_spectrum_by_mass(self.user_config) - doppler_shift_and_resampling = get_doppler_shift_and_resampling( - self.user_config - ) apply_extinction = get_extinction(self.user_config) - calculate_datacube = get_calculate_datacube(self.user_config) calculate_datacube_particlewise = get_calculate_datacube_particlewise( self.user_config ) @@ -121,12 +110,8 @@ def _get_pipeline_functions(self) -> list: rotate_galaxy, filter_particles, spaxel_assignment, - calculate_spectra, # reshape_data, - scale_spectrum_by_mass, - doppler_shift_and_resampling, apply_extinction, - calculate_datacube, calculate_datacube_particlewise, convolve_psf, convolve_lsf, @@ -231,6 +216,7 @@ def run_sharded(self, inputdata): # if the particle number is not modulo the device number, we have to pad a few empty particles # to make it work + n = inputdata.stars.coords.shape[0] pad = (num_devices - (n % num_devices)) % num_devices if pad: self.logger.info( diff --git a/tests/test_core_ifu.py b/tests/test_core_ifu.py index e54ba5a2..21f67f7f 100644 --- a/tests/test_core_ifu.py +++ b/tests/test_core_ifu.py @@ -1,19 +1,8 @@ -import jax import jax.numpy as jnp import numpy as np -from rubix.core.data import Galaxy, GasData, RubixData, StarsData, reshape_array -from rubix.core.ifu import ( - get_calculate_datacube_particlewise, - get_calculate_spectra, - get_doppler_shift_and_resampling, - get_resample_spectrum_vmap, - get_scale_spectrum_by_mass, - get_telescope, - get_velocities_doppler_shift_vmap, -) -from rubix.core.ssp import get_ssp -from rubix.spectra.ifu import resample_spectrum, velocity_doppler_shift +from rubix.core.data import Galaxy, GasData, RubixData, StarsData +from rubix.core.ifu import get_calculate_datacube_particlewise, get_telescope RTOL = 1e-4 ATOL = 1e-6 @@ -79,235 +68,6 @@ def __init__(self, spectra): target_wavelength = jnp.array([4000.0, 5000.0, 6000.0]) -def _get_sample_inputs(subset=None): - ssp = get_ssp(sample_config) - """metallicity = reshape_array(ssp.metallicity) - age = reshape_array(ssp.age) - spectra = reshape_array(ssp.flux)""" - metallicity = ssp.metallicity - age = ssp.age - spectra = ssp.flux - - print("Metallicity shape: ", metallicity.shape) - print("Age shape: ", age.shape) - print("Spectra shape: ", spectra.shape) - print(".............") - - # Create meshgrid for metallicity and age to cover all combinations - metallicity_grid, age_grid = np.meshgrid( - metallicity.flatten(), age.flatten(), indexing="ij" - ) - metallicity_grid = jnp.asarray(metallicity_grid.flatten()) # Convert to jax.Array - age_grid = jnp.asarray(age_grid.flatten()) # Convert to jax.Array - metallicity_grid = reshape_array(metallicity_grid) - age_grid = reshape_array(age_grid) - metallicity_grid = jnp.array(metallicity_grid) - age_grid = jnp.array(age_grid) - print("Metallicity grid shape: ", metallicity_grid.shape) - print("Age grid shape: ", age_grid.shape) - - spectra = spectra.reshape(-1, spectra.shape[-1]) - print("spectra after reshape: ", spectra.shape) - spectra = reshape_array(spectra) - - print("spectra after reshape_array call: ", spectra.shape) - - # reshape spectra - num_combinations = metallicity_grid.shape[1] - spectra_reshaped = spectra.reshape( - spectra.shape[0], num_combinations, spectra.shape[-1] - ) - - # Create Velocities for each combination - - velocities = jnp.ones((metallicity_grid.shape[0], num_combinations, 3)) - mass = jnp.ones_like(metallicity_grid) - - if subset is not None: - metallicity_grid = metallicity_grid[:, :subset] - age_grid = age_grid[:, :subset] - velocities = velocities[:, :subset] - mass = mass[:, :subset] - spectra_reshaped = spectra_reshaped[:, :subset] - # inputs = dict( - # metallicity=metallicity_grid, age=age_grid, velocities=velocities, mass=mass - # ) - inputs = MockRubixData( - MockStarsData( - velocity=velocities, - metallicity=metallicity_grid, - mass=mass, - age=age_grid, - ), - MockGasData(spectra=None), - ) - return inputs, spectra_reshaped - - -def test_resample_spectrum_vmap(): - print("initial_spectra shape", initial_spectra.shape) - print("initial_wavelengths shape", initial_wavelengths.shape) - print("target_wavelength shape", target_wavelength.shape) - resample_spectrum_vmap = get_resample_spectrum_vmap(target_wavelength) - result_vmap = resample_spectrum_vmap(initial_spectra, initial_wavelengths) - - expected_result = jnp.stack( - [ - resample_spectrum( - initial_spectra[0], initial_wavelengths[0], target_wavelength - ), - resample_spectrum( - initial_spectra[1], initial_wavelengths[1], target_wavelength - ), - ] - ) - assert jnp.allclose(result_vmap, expected_result) - assert not jnp.any(jnp.isnan(result_vmap)) - - -def test_calculate_spectra(): - # Use an actual RubixData instance - mock_rubixdata = RubixData( - galaxy=Galaxy(), - stars=StarsData(), - gas=GasData(), - ) - - # Populate the RubixData object with mock data - mock_rubixdata.stars.coords = jnp.array([1, 2, 3]) - mock_rubixdata.stars.velocity = jnp.array([4.0, 5.0, 6.0]) - mock_rubixdata.stars.metallicity = jnp.array( - [0.1] - ) # 2D array for vmap compatibility - mock_rubixdata.stars.mass = jnp.array([1000]) # 2D array for vmap compatibility - mock_rubixdata.stars.age = jnp.array([4.5]) # 2D array for vmap compatibility - mock_rubixdata.galaxy.redshift = 0.1 - mock_rubixdata.galaxy.center = jnp.array([0, 0, 0]) - mock_rubixdata.galaxy.halfmassrad_stars = 1 - - # Obtain the calculate_spectra function - calculate_spectra = get_calculate_spectra(sample_config) - - # Mock expected spectra - expected_spectra_shape = (1, 842) # Adjust shape as per your data - expected_spectra = jnp.zeros(expected_spectra_shape) - - # Call the calculate_spectra function - result = calculate_spectra(mock_rubixdata) - - # Validate the result - calculated_spectra = result.stars.spectra - - assert calculated_spectra.shape == expected_spectra.shape, "Shape mismatch" - assert jnp.allclose( - calculated_spectra, expected_spectra, rtol=RTOL, atol=ATOL - ), "Spectra values mismatch" - assert not jnp.any( - jnp.isnan(calculated_spectra) - ), "NaN values in calculated spectra" - - -def test_scale_spectrum_by_mass(): - # Use an actual RubixData instance - input = RubixData( - galaxy=Galaxy(), - stars=StarsData( - velocity=sample_inputs["velocities"], - metallicity=sample_inputs["metallicity"], - mass=sample_inputs["mass"], - age=sample_inputs["age"], - spectra=sample_inputs["spectra"], - ), - gas=GasData(spectra=None), - ) - - # Calculate expected spectra - expected_spectra = input.stars.spectra * jnp.expand_dims(input.stars.mass, -1) - - # Call the function - scale_spectrum_by_mass = get_scale_spectrum_by_mass(sample_config) - result = scale_spectrum_by_mass(input) - - # Print for debugging - print("Input Mass:", input.stars.mass) - print("Input Spectra:", input.stars.spectra) - print("Result Spectra:", result.stars.spectra) - print("Expected Spectra:", expected_spectra) - - # Assertions - assert jnp.array_equal( - result.stars.spectra, expected_spectra - ), "Spectra scaling mismatch" - assert not jnp.any( - jnp.isnan(result.stars.spectra) - ), "NaN values found in result spectra" - - -def test_get_velocities_doppler_shift_vmap(): - # 1) Setup a small SSP wavelength grid - ssp_wave = jnp.array([4000.0, 5000.0, 6000.0]) - - # 2) Build the vmap‐wrapped doppler function - doppler_fn = get_velocities_doppler_shift_vmap(ssp_wave, velocity_direction="x") - - # ——— Zero‐velocity case ——— - velocities_zero = jnp.zeros((4, 3)) # 4 particles, all zero velocity - out_zero = doppler_fn(velocities_zero) - # Compare to a direct call on the full batch: - expected_zero = velocity_doppler_shift(ssp_wave, velocities_zero, direction="x") - # shape & values should match, and every row must equal the original grid - assert out_zero.shape == expected_zero.shape - assert jnp.allclose(out_zero, expected_zero, rtol=RTOL, atol=ATOL) - assert jnp.allclose(out_zero, ssp_wave, rtol=RTOL, atol=ATOL) - - # ——— Non‐zero velocities ——— - velocities = jnp.array( - [ - [1000.0, 0.0, 0.0], - [-1000.0, 0.0, 0.0], - ] - ) - out = doppler_fn(velocities) - - # Now compare to a single batch call - expected = velocity_doppler_shift(ssp_wave, velocities, direction="x") - assert out.shape == expected.shape, "Shape mismatch between vmap and direct call" - assert jnp.allclose( - out, expected, rtol=RTOL, atol=ATOL - ), "Values diverge from direct call" - assert not jnp.any(jnp.isnan(out)), "Found NaNs in the doppler‐shifted output" - - -def test_doppler_shift_and_resampling(): - # Obtain the function - doppler_shift_and_resampling = get_doppler_shift_and_resampling(sample_config) - - # Create an actual RubixData object - inputs = RubixData( - galaxy=Galaxy(), # Create a Galaxy instance as required - stars=StarsData( - velocity=sample_inputs["velocities"], - metallicity=sample_inputs["metallicity"], - mass=sample_inputs["mass"], - age=sample_inputs["age"], - spectra=sample_inputs["spectra"], # Assign expected spectra - ), - gas=GasData(spectra=None), - ) - - # Mock expected spectra - expected_spectra = sample_inputs["spectra"] - - # Call the function - result = doppler_shift_and_resampling(inputs) - - # Assertions - assert hasattr(result.stars, "spectra"), "Result does not have 'spectra'" - assert not jnp.any( - jnp.isnan(result.stars.spectra) - ), "NaN values found in result spectra" - - def test_get_calculate_datacube_particlewise(): # Setup config and telescope config = { diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index 4c76e94b..0c43b82a 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -5,12 +5,7 @@ import jax.numpy as jnp import pytest -from rubix.core.data import ( - Galaxy, - GasData, - RubixData, - StarsData, -) +from rubix.core.data import Galaxy, GasData, RubixData, StarsData from rubix.core.pipeline import RubixPipeline from rubix.spectra.ssp.grid import SSPGrid from rubix.telescope.base import BaseTelescope @@ -92,101 +87,6 @@ def test_rubix_pipeline_not_implemented(setup_environment): pipeline = RubixPipeline(user_config=config) # noqa -""" -def test_rubix_pipeline_gradient_not_implemented(setup_environment): - pipeline = RubixPipeline(user_config=user_config) - with pytest.raises( - NotImplementedError, match="Gradient calculation is not implemented yet" - ): - pipeline.gradient() -""" - -""" -def test_rubix_pipeline_gradient_not_implemented(setup_environment): - mock_rubix_data = MagicMock() - mock_rubix_data.stars.coords = jnp.array([[0, 0, 0]]) - mock_rubix_data.stars.velocities = jnp.array([[0, 0, 0]]) - mock_rubix_data.stars.metallicity = jnp.array([0.1]) - mock_rubix_data.stars.mass = jnp.array([1.0]) - mock_rubix_data.stars.age = jnp.array([1.0]) - - with patch("rubix.core.pipeline.get_rubix_data", return_value=mock_rubix_data): - pipeline = RubixPipeline(user_config=user_config) - with pytest.raises( - NotImplementedError, match="Gradient calculation is not implemented yet" - ): - pipeline.gradient() -""" - - -def test_rubix_pipeline_run(): - # Mock input data for the function - input_data = RubixData( - galaxy=Galaxy( - redshift=jnp.array([0.1]), - center=jnp.array([[0.0, 0.0, 0.0]]), - halfmassrad_stars=jnp.array([1.0]), - ), - stars=StarsData( - coords=jnp.array([[1.0, 2.0, 3.0], [3.0, 4.0, 5.0]]), - velocity=jnp.array([[5.0, 6.0, 7.0], [7.0, 8.0, 9.0]]), - metallicity=jnp.array([0.1, 0.2]), - mass=jnp.array([1000.0, 2000.0]), - age=jnp.array([4.5, 5.5]), - pixel_assignment=jnp.array([0, 1]), - ), - gas=GasData(velocity=None), - ) - - pipeline = RubixPipeline(user_config=user_config) - output = pipeline.run(input_data) - - # Check if output is as expected - assert hasattr(output.stars, "coords") - assert hasattr(output.stars, "velocity") - assert hasattr(output.stars, "metallicity") - assert hasattr(output.stars, "mass") - assert hasattr(output.stars, "age") - assert hasattr(output.stars, "spectra") - - assert isinstance(pipeline.telescope, BaseTelescope) - assert isinstance(pipeline.ssp, SSPGrid) - - spectrum = output.stars.spectra - print("Spectrum shape: ", spectrum.shape) - print("Spectrum sum: ", jnp.sum(spectrum, axis=-1)) - - # Check if spectrum contains any nan values - # Only count the numby of NaN values in the spectra - is_nan = jnp.isnan(spectrum) - # check whether there are any NaN values in the spectra - - indices_nan = jnp.where(is_nan) - - # Get only the unique index of the spectra with NaN values - unique_spectra_indices = jnp.unique(indices_nan[-1]) - print("Unique indices of spectra with NaN values: ", unique_spectra_indices) - print( - "Masses of the spectra with NaN values: ", - output.stars.mass[unique_spectra_indices], - ) - print( - "Ages of the spectra with NaN values: ", - output.stars.age[unique_spectra_indices], - ) - print( - "Metallicities of the spectra with NaN values: ", - output.stars.metallicity[unique_spectra_indices], - ) - - ssp = pipeline.ssp - print("SSP bounds age:", ssp.age.min(), ssp.age.max()) - print("SSP bounds metallicity:", ssp.metallicity.min(), ssp.metallicity.max()) - - # assert that the spectra does not contain any NaN values - assert not jnp.isnan(spectrum).any() - - def test_rubix_pipeline_run_sharded(): # Use the number of devices to set up data that can be sharded num_devices = len(jax.devices()) From 8c8aaf2a2bc631bda907896cdd026e988ad5112b Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 4 Nov 2025 11:15:06 +0100 Subject: [PATCH 70/76] update core data regarding Haralds and Tobias comments --- notebooks/rubix_pipeline_sharding.py | 114 ---- ...bix_pipeline_single_function_scaling.ipynb | 377 ------------ ...x_pipeline_single_function_shard_map.ipynb | 212 +++++-- ...eline_single_function_shard_map_fits.ipynb | 542 ------------------ ...ine_single_function_shard_map_memory.ipynb | 517 ----------------- rubix/core/data.py | 119 ++-- 6 files changed, 257 insertions(+), 1624 deletions(-) delete mode 100644 notebooks/rubix_pipeline_sharding.py delete mode 100644 notebooks/rubix_pipeline_single_function_scaling.ipynb delete mode 100644 notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb delete mode 100644 notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb diff --git a/notebooks/rubix_pipeline_sharding.py b/notebooks/rubix_pipeline_sharding.py deleted file mode 100644 index cfbbe6cd..00000000 --- a/notebooks/rubix_pipeline_sharding.py +++ /dev/null @@ -1,114 +0,0 @@ -import os - -# os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3' - -# Specify the number of GPUs to use -# os.environ['CUDA_VISIBLE_DEVICES'] = "1,4,5,8,9" - -# os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false" - -# Set the FSPS path to the template files -# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps' -# os.environ['SPS_HOME'] = '/home/annalena/sps_fsps' -# os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps' -# os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps' -os.environ["SPS_HOME"] = "/home/annalena_data/sps_fsps" - -import jax -import jax.numpy as jnp -import matplotlib.pyplot as plt - -from rubix.core.pipeline import RubixPipeline - -# Now JAX will list two CpuDevice entries -print(jax.devices()) - - -config = { - "pipeline": {"name": "calc_ifu"}, - "logger": { - "log_level": "DEBUG", - "log_file_path": None, - "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", - }, - "data": { - "name": "IllustrisAPI", - "args": { - "api_key": os.environ.get("ILLUSTRIS_API_KEY"), - "particle_type": ["stars"], - "simulation": "TNG50-1", - "snapshot": 99, - "save_data_path": "data", - }, - "load_galaxy_args": { - "id": 14, - "reuse": True, - }, - "subset": { - "use_subset": True, - "subset_size": 10000, - }, - }, - "simulation": { - "name": "IllustrisTNG", - "args": { - "path": "data/galaxy-id-14.hdf5", - }, - }, - "output_path": "output", - "telescope": { - "name": "MUSE", - "psf": {"name": "gaussian", "size": 5, "sigma": 0.6}, - "lsf": {"sigma": 0.5}, - "noise": {"signal_to_noise": 100, "noise_distribution": "normal"}, - }, - "cosmology": {"name": "PLANCK15"}, - "galaxy": { - "dist_z": 0.1, - "rotation": {"type": "edge-on"}, - }, - "ssp": { - "template": {"name": "FSPS"}, - "dust": { - "extinction_model": "Cardelli89", - "dust_to_gas_ratio": 0.01, - "dust_to_metals_ratio": 0.4, - "dust_grain_density": 3.5, - "Rv": 3.1, - }, - }, -} - -pipe = RubixPipeline(config) -inputdata = pipe.prepare_data() -rubixdata = pipe.run_sharded(inputdata) - - -# Plotting the spectra -wave = pipe.telescope.wave_seq - -plt.figure(figsize=(10, 5)) -plt.title("Spectra of a single star") -plt.xlabel("Wavelength (Angstroms)") -plt.ylabel("Luminosity") -# spectra = rubixdata.stars.datacube # Spectra of all stars -spectra = rubixdata -plt.plot(wave, spectra[12, 12, :]) -plt.plot(wave, spectra[12, 14, :]) -plt.savefig("./output/rubix_spectra.jpg") -plt.close() - -plt.figure(figsize=(6, 5)) -# get the indices of the visible wavelengths of 4000-8000 Angstroms -visible_indices = jnp.where((wave >= 4000) & (wave <= 8000)) -# visible_spectra = rubixdata.stars.datacube[:, :, visible_indices[0]] -visible_spectra = rubixdata[:, :, visible_indices[0]] -# Sum up all spectra to create an image -image = jnp.sum(visible_spectra, axis=2) -plt.imshow(image, origin="lower", cmap="inferno") -plt.colorbar() -plt.title("Image of the galaxy") -plt.xlabel("X pixel") -plt.ylabel("Y pixel") -plt.savefig("./output/rubix_image.jpg") -plt.close() diff --git a/notebooks/rubix_pipeline_single_function_scaling.ipynb b/notebooks/rubix_pipeline_single_function_scaling.ipynb deleted file mode 100644 index 2def84d5..00000000 --- a/notebooks/rubix_pipeline_single_function_scaling.ipynb +++ /dev/null @@ -1,377 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from jax import config\n", - "#config.update(\"jax_enable_x64\", True)\n", - "config.update('jax_num_cpu_devices', 2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import os\n", - "\n", - "# Tell XLA to fake 2 host CPU devices\n", - "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", - "\n", - "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '1,3,4,5,6,7,8,9'\n", - "\n", - "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", - "\n", - "import jax\n", - "\n", - "# Now JAX will list two CpuDevice entries\n", - "print(jax.devices())\n", - "# → [CpuDevice(id=0), CpuDevice(id=1)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "#import os\n", - "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RUBIX pipeline\n", - "\n", - "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", - "\n", - "## How to use the Pipeline\n", - "1) Define a `config`\n", - "2) Setup the `pipeline yaml`\n", - "3) Run the RUBIX pipeline\n", - "4) Do science with the mock-data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Config\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "from rubix.core.pipeline import RubixPipeline \n", - "import os\n", - "\n", - "config_TNG = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"IllustrisAPI\",\n", - " \"args\": {\n", - " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", - " \"particle_type\": [\"stars\"],\n", - " \"simulation\": \"TNG50-1\",\n", - " \"snapshot\": 99,\n", - " \"save_data_path\": \"data\",\n", - " },\n", - " \n", - " \"load_galaxy_args\": {\n", - " \"id\": 11,\n", - " \"reuse\": True,\n", - " },\n", - " \n", - " \"subset\": {\n", - " \"use_subset\": True,\n", - " \"subset_size\": 1,\n", - " },\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"IllustrisTNG\",\n", - " \"args\": {\n", - " \"path\": \"data/galaxy-id-11.hdf5\",\n", - " },\n", - " \n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.1,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"FSPS\", #\"Mastar_CB19_SLOG_1_5\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Run the pipeline\n", - "\n", - "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config_TNG)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import jax.numpy as jnp\n", - "\n", - "inputdata = pipe.prepare_data()\n", - "#inputdata = pipe.prepare_data()\n", - "coords = inputdata.stars.coords\n", - "vel = inputdata.stars.velocity\n", - "mass = inputdata.stars.mass\n", - "age = inputdata.stars.age\n", - "met = inputdata.stars.metallicity\n", - "factor = 1\n", - "inputdata.stars.coords = jnp.concatenate([coords]*factor, axis=0)\n", - "inputdata.stars.velocity = jnp.concatenate([vel]*factor, axis=0)\n", - "inputdata.stars.mass = jnp.concatenate([mass]*factor, axis=0)\n", - "inputdata.stars.age = jnp.concatenate([age]*factor, axis=0)\n", - "inputdata.stars.metallicity = jnp.concatenate([met]*factor, axis=0)\n", - "inputdata.stars.coords.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "\n", - "rubixdata = pipe.run_sharded(inputdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "rubixdata = pipe.run_sharded(inputdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import jax.numpy as jnp\n", - "gpu_number = jnp.array([1, 2, 3, 4, 5, 6, 7])\n", - "time_on_compgpu4_5e5mal2 = jnp.array([274.27, 152.38, 108.70, 88.38, 88.97, 71.85, 62.91])\n", - "time_on_compgpu4_5e5mal1 = jnp.array([151.12, 77.44, 63.81, 50.88, 48.34, 48.1, 41.60])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(5,3))\n", - "plt.plot(gpu_number, time_on_compgpu4_5e5mal2, marker='o', label='1e6 particles')\n", - "plt.plot(gpu_number, time_on_compgpu4_5e5mal1, marker='o', label='5e5 particles')\n", - "plt.xlabel('Number of GPUs')\n", - "plt.ylabel('Time in seconds on RTX 2080ti')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import jax.numpy as jnp\n", - "particle_number = jnp.array([1, 10, 100, 1e3, 1e4, 1e5, 5e5, 5e5*2, 5e5*20, 5e5*50, 5e5*100, 5e5*150, 5e5*200, 5e5*300, 5e5*400])\n", - "time_on_mac_2cpu = jnp.array([2.14, 2.14, 2.24, 2.2, 2.2 ,2.15, 2.34, 2.26, 2.50, 3.78, 16.88, 38.92, 56.29, 72.27, 86.98]) #seconds\n", - "particle_number_gpu = jnp.array([1, 10, 100, 1e3, 1e4, 1e5, 5e5, 5e5*2, 5e5*4, 5e5*20])\n", - "time_on_compgpu4_2gpu = jnp.array([18.01, 18.64, 18.44, 18.58, 20.43, 31.14, 84.95, 138.29, 255.22, 1182.35])\n", - "time_on_compgpu4_4gpu = jnp.array([19.11, 19.18, 19.69, 19.26, 20.74, 27.97, 59.56, 89.58, 142.98, 707.86])\n", - "time_on_compgpu4_6gpu = jnp.array([20.14, 20.22, 20.34, 20.85, 20.48, 25.59, 47.19, 76.89, 122.12, 500.78])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(5, 3))\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu, marker='o', label='2 GPUs')\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_4gpu, marker='o', label='4 GPUs')\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_6gpu, marker='o', label='6 GPUs')\n", - "plt.xlabel('Number of particles')\n", - "plt.ylabel('Time in seconds on RTX 2080ti')\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(5, 3))\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu, marker='o', label='2 GPUs')\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_4gpu, marker='o', label='4 GPUs')\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_6gpu, marker='o', label='6 GPUs')\n", - "plt.plot(particle_number, time_on_mac_2cpu, marker='x', label='MacBook Pro 2 CPUs')\n", - "plt.xlabel('Number of particles')\n", - "plt.ylabel('Time in seconds')\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(5, 3))\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_2gpu/particle_number_gpu, marker='o', label='2 GPUs')\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_4gpu/particle_number_gpu, marker='o', label='4 GPUs')\n", - "plt.plot(particle_number_gpu, time_on_compgpu4_6gpu/particle_number_gpu, marker='o', label='6 GPUs')\n", - "plt.xlabel('Number of particles')\n", - "plt.ylabel('Time per particle in seconds')\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(5, 3))\n", - "plt.plot(particle_number, time_on_mac_2cpu, marker='o', linestyle='-')\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "plt.xlabel('Number of particles')\n", - "plt.ylabel('Time in seconds on M1')\n", - "# plt.title('Scaling of Rubix Pipeline with Number of Particles')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(5, 3))\n", - "plt.plot(particle_number, time_on_mac_2cpu/particle_number, marker='o', linestyle='-')\n", - "plt.xscale('log')\n", - "plt.yscale('log')\n", - "plt.xlabel('Number of particles')\n", - "plt.ylabel('Time per particle in seconds')\n", - "# plt.title('Scaling of Rubix Pipeline with Number of Particles')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index f4d2ca04..11d76711 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -20,9 +20,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0)]\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -44,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -52,9 +60,9 @@ "#import os\n", "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", + "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", - "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" + "#os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" ] }, { @@ -105,9 +113,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-04 11:13:09,119 - rubix - INFO - \n", + " ___ __ _____ _____ __\n", + " / _ \\/ / / / _ )/ _/ |/_/\n", + " / , _/ /_/ / _ |/ /_> <\n", + "/_/|_|\\____/____/___/_/|_|\n", + "\n", + "\n", + "2025-11-04 11:13:09,120 - rubix - INFO - Rubix version: 0.0.post503+g060c53b49.d20251002\n", + "2025-11-04 11:13:09,120 - rubix - INFO - JAX version: 0.4.38\n", + "2025-11-04 11:13:09,120 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -174,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -198,19 +223,19 @@ " },\n", " \n", " \"load_galaxy_args\": {\n", - " \"id\": 14,\n", + " \"id\": 12,\n", " \"reuse\": True,\n", " },\n", " \n", " \"subset\": {\n", " \"use_subset\": True,\n", - " \"subset_size\": 200000,\n", + " \"subset_size\": 2000,\n", " },\n", " },\n", " \"simulation\": {\n", " \"name\": \"IllustrisTNG\",\n", " \"args\": {\n", - " \"path\": \"data/galaxy-id-14.hdf5\",\n", + " \"path\": \"data/galaxy-id-12.hdf5\",\n", " },\n", " \n", " },\n", @@ -330,19 +355,118 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config_NIHAO)" + "pipe = RubixPipeline(config_TNG)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-04 11:13:09,998 - rubix - INFO - Getting rubix data...\n", + "2025-11-04 11:13:09,999 - rubix - INFO - Loading data from IllustrisAPI\n", + "2025-11-04 11:13:10,000 - rubix - INFO - Reusing existing file galaxy-id-12.hdf5. If you want to download the data again, set reuse=False.\n", + "2025-11-04 11:13:10,021 - rubix - INFO - Loading data into input handler\n", + "2025-11-04 11:13:10,022 - rubix - DEBUG - Loading data from Illustris file..\n", + "2025-11-04 11:13:10,022 - rubix - DEBUG - Checking if the fields are present in the file...\n", + "2025-11-04 11:13:10,023 - rubix - DEBUG - Keys in the file: \n", + "2025-11-04 11:13:10,023 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", + "2025-11-04 11:13:10,023 - rubix - DEBUG - Matching fields: {'Header', 'SubhaloData', 'PartType4'}\n", + "2025-11-04 11:13:10,026 - rubix - DEBUG - Found 649384 valid particles out of 649384\n", + "2025-11-04 11:13:10,233 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", + "2025-11-04 11:13:14,951 - rubix - DEBUG - Converting to Rubix format..\n", + "2025-11-04 11:13:14,951 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", + "2025-11-04 11:13:14,951 - rubix - DEBUG - Keys in the particle data: dict_keys(['stars'])\n", + "2025-11-04 11:13:14,951 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", + "2025-11-04 11:13:14,952 - rubix - DEBUG - Matching fields: {'stars'}\n", + "2025-11-04 11:13:14,952 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", + "2025-11-04 11:13:14,952 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", + "2025-11-04 11:13:14,952 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-11-04 11:13:14,953 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", + "2025-11-04 11:13:14,956 - rubix - DEBUG - Converting redshift for galaxy data into \n", + "2025-11-04 11:13:14,957 - rubix - DEBUG - Converting center for galaxy data into kpc\n", + "2025-11-04 11:13:14,957 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", + "2025-11-04 11:13:14,958 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", + "2025-11-04 11:13:14,969 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", + "2025-11-04 11:13:15,004 - rubix - DEBUG - Converting metallicity for particle type stars into \n", + "2025-11-04 11:13:15,008 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", + "2025-11-04 11:13:15,019 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", + "2025-11-04 11:13:15,028 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", + "2025-11-04 11:13:15,074 - rubix - INFO - Centering stars particles\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted to Rubix format!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-11-04 11:13:15,657 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", + "2025-11-04 11:13:15,658 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", + "2025-11-04 11:13:15,659 - rubix - INFO - Data preparation completed in 5.66 seconds.\n", + "2025-11-04 11:13:15,659 - rubix - INFO - Setting up the pipeline...\n", + "2025-11-04 11:13:15,659 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", + "2025-11-04 11:13:15,660 - rubix - DEBUG - Rotation Type found: edge-on\n", + "2025-11-04 11:13:15,661 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-04 11:13:15,669 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-04 11:13:15,812 - rubix - INFO - Calculating spatial bin edges...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-04 11:13:15,819 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-04 11:13:15,826 - rubix - INFO - Getting cosmology...\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-04 11:13:16,084 - rubix - DEBUG - Method not defined, using default method: cubic\n", + "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", + " warnings.warn(\n", + "2025-11-04 11:13:16,489 - rubix - INFO - Assembling the pipeline...\n", + "2025-11-04 11:13:16,490 - rubix - INFO - Compiling the expressions...\n", + "2025-11-04 11:13:16,490 - rubix - INFO - Number of devices: 1\n", + "2025-11-04 11:13:16,610 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", + "2025-11-04 11:13:16,611 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", + "2025-11-04 11:13:16,611 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", + "2025-11-04 11:13:16,653 - rubix - INFO - Filtering particles outside the aperture...\n", + "2025-11-04 11:13:16,655 - rubix - INFO - Assigning particles to spaxels...\n", + "2025-11-04 11:13:16,663 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", + "2025-11-04 11:13:16,810 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", + "2025-11-04 11:13:16,811 - rubix - INFO - Convolving with PSF...\n", + "2025-11-04 11:13:16,812 - rubix - INFO - Convolving with LSF...\n", + "2025-11-04 11:13:16,814 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", + "2025-11-04 11:13:17,748 - rubix - INFO - Sharding completed in 0.84 seconds.\n", + "2025-11-04 11:13:17,749 - rubix - INFO - Sharded pipeline run completed in 1.25 seconds.\n", + "2025-11-04 11:13:17,749 - rubix - INFO - Total time for sharded pipeline run: 2.09 seconds.\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "\n", @@ -352,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -373,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -394,9 +518,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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fJNJzLtx1b/bWU+pnik0gpWfHRNPCrNWBs2kXdVxERERERFVB9cou5/vhhx8wc+ZMNUtK1jTJKTNTCwCkfE868EkXv6NHj2LOnDmqtbl09GvTpo3aR9qlS8B0++23Y9u2bZg/fz6ef/55dd+SeXI1thmp9Gzry6VxOD4Nwz5ZhZ7/9595W5pN+/L64Zb28rZzq2zL+Iyuax+lfq44kGAOhiU7JT9FTl4B5u2MxYMzNiGpnLsOEhERERFdloHU1KlTVac+GborGSb99PPPP6vrpXW5tDWXYKlZs2Z44oknMHLkSPz111/m+3Bzc1NlgfJTslO33XabCraMc6dciXGNlMjMLf38Juny992ao1h35JzVdglyUm0CqYiAokHmsXMZdkv7dO7Vq+GWznVRrRqw72yqKvebsvQQOr22CJ8tP4y8/AIMen8ZHvhhM+buOIN35lsPVxZp2XksCyQiIiIil1epzSb0LEZxpAGEDO29EOnqN3fuXFQFtoGUIxmpJ3/dZs4UGec9Df90VakCqVOFrc5ts1e6F69tgRA/T7SpE4xtJ5Kw+lACvl19VF33f//uxfUdauNYoqU1/YG4ouV/o79ch8NxaVj0RF9EBnqX+rkRERERETkTp2g2QfbnSOnDcUvLNogSS/fFY++ZVKv1UiIyoGgQo6/HKm49VY3CwKdVVKC5bXqeoYmF7dqq9UfOIcNwX9I4QwKw1Ow8LN5jWdNGRERERORqGEg5mazcgiKlcJciMd1+GV2NwKIZqczcfJXByigmC6avq4oJ8zWXAsqMKd36I+eL3Ob+7zfZHTC8JzYFP284fsnPj4iIiIioMjCQctKMlLeH9k/zyt+7sWDXGXMjh69WHsGxROsBuiV57o+ddrfXsFPaJ9Kz84rNSDUp7NhXN1QLqI4npiO/wBL47TujtU8vLksWl2oJpL5fewxP/b4Dk2fbPz4iIiIiImfGQMrJZBdmpEJ8Pc3b7ivM6jz3xw68+vduPDRzi93berqX/p9TMkkBXkWXyJ04nwl7I6cGNo80nzdmpDIMpYfSgKIkp5OsZ1GJP7ZoLdeNDsWnFSlxJCIiIiJyJgyknEx6YWAS7l80Y/TvTi0zteNUst3b+tsJjIrj6+mG5ZOuKLJdGlPY88p1Lc3nawVpa6WSMnKt1kUVN1tKuvkVFJhUOZ890mnwvu82Yu3hRCzbH48B/1uGCT9vK/VzISIiIiK6rLr2UVHJhbOXooK9iwRMKuNUQufw4rrt2ePn6a468JXGrw90R1Swj/lyoLcH3KpXQ36BzJCyzIpKLGZ4sLRTf/Pfvfht00m717/w5y71c8Hus+jbJEKd/2dHLD4t9bMhIiIiIqpYzEg5maTCDE/tYEsTB51XCaV70iQiJ9+6UUVJgn091M8WtbQOfBfKXhlVr17NqvTQ1sgOddA2Oth8edvJJKsg6pkhzdQsqtI8FhERERGRM2Ig5aRzpMIDigYq1YuLPi7Q3e+tka2LbAvw1gKpL8d0wiMDGqO2IeNky8+zaOIy3L/4QEqu+3N8T9Qt7Og39puN5usGNq+B+/o0wK6XB+PwG1ejUQ1/q9sWGGaLSXBIREREROSMGEg5ERlQLOVyevmcveuLow/c9XQr+k9qO/j2nl71VWmekJK9CYOaoF540QxYSVmi0BLKAvW1Wralg9Kk4ssxnVGtWjX4erqrzJYM+TWav+us+fy0ZYcwYsoqxKVkqXlU0ulPygTFwbg0XPfJSny/RhsITERERERUkbhGyokYu+UF+niUOogyro8K8/dEbLJ1d7yahc0hxHdju6BP4TokI7fq1gHY2J718fWqI+q8r50mFiWV9gV4FwZSheWDuhs61Cmyb+/GEfj01g4YP3Nzkes+WHRA/ezyxmLzttUHEzD1to7q2LadTFanoW2iSgzsiIiIiIjKGjNSTkTPRgl/L+ss0IXWP6Vm5ZqzQV+N6WR1nWybdFVT3N2rPno2Crd7+0Nxlo57sx7sgfoR2qwo4eNRNCMV4lc0Y2Z+vMJsmjHYigryxgP9Gtrdf2ibWuYywAvROxeeNQSL0piCiIiIiKgiMZBy0kDKNgGlr53SSTtxo9TCNVKSDRrQPBLNDU0kpNvfg/0aYfI1Lcwlfbbu6llP/byiaQQ61A1Bs5ra8F1h7zalyUjpDS3E3b0bwMNO2aHl+ZVuPZQe1J1KyjRviy08/8Xywxj60QrsOm2/PTwRERERUVlhaZ8TyTdET81suunl5BXAGDrlFhTAq7pbkTVSejbIGPrYWzdl684e9dAwwh/dG4apy53rheKz2zsWGzAF2ZQe2rsu1HDbkppT6M+vNLw8qqsyx5PnLYFUSlYudp9Owetz96jLny8/jA9vbl+q+yMiIiIiuhjMSDmR/HxLqFQjwAtLn+xXbEbKNvBIKyzt07NBxu53av7UBbi7VccVzWrA21DGN7hlTXSpH2p3f+NcqeKaWwQb1i1FBBQdMGyUa3ju9kwc3NQ8BPhceo5Vl8KUzDzMWHfMfPlIQnqJ90VEREREdKkYSDlpRsqtWjXUC/dDQGGjB5WRMpUQSOmlfYX7G/ctTUbKURJkFadOiBZkBRuyVjVtOgfaen1EK/WzY0wIejbSsmLG47+mTS3oFYYyuNfofEaOee2UHkhJ1kpeo4d/3IKnfttepBSSiIiIiOhSsLTPieQVaMGRjIuS1uB6KVtqtraGKNfQcMK2+YS5tE8PpAyFgJJtKmuybqpNnSBsP2m9Hqlf0wjzWqj64X7m4O5CzSSua1cbXeuHITLQCx8tPohVBxPV9vv7NlDd/mLC/BDq54WEtGy8WVjCp1t/5JzK2MnAYvkpr4VkrbafSsZf206rfW7uEq3u43RSJlrVDirT14KIiIiILj/MSDmRwjhKZaNss0mSXTFmoWwzUpZmE1oWqCISMO/d1FYN1P3w5nbmdVFXGTJVErD8cn93/PlQz1IFc9KmXWZMBflY4vtWUUFoHBlgtc4qpTBobF0YEOllj53qhajugGLn6RS1bkq37UQShny4HNd+slLNoCIiIiIiuhTMSDlhRsrYJc+rcM2SBAvZJQVS5mYTRddIlZdGNQKwaEJfdV46/W0+fh7Xtomy2qe4NVYlCTJ0+9PXfIlwf1lnlWq+fFu3unjq9x1WQVdungmnk7Mw5uv1aFvHknlatj8eZ1Oy1fktx8+rAJCIiIiI6GIxkHLGjFT1ohmp7NwCq3K+7As0m6iAOMpKdKivOpUFY0dAPcOmDxs2ahllXaJ3Q8c6VqWGMqxXt/qQViooztgMLCYiIiIichRL+5yw2YRVIFXYcc/Ypa6kNVJ6s4mKyEiVF2MgZSzz0zJSFsasUsuoQFUCOLpbXbv3aQw8z6YykCIiIiKiS8NAyonk2yntKzaQKq5rn3mNlOsGUoHe9jNSoYZ26sLYql2/zdDWtTCweQ3zdmleYeuHtceRmpWL3zedxOqDCWV+/ERERERU9bG0z8lIRknvvGcs7dNL93R5NnOXbNdIyVohV+XjaQmQjK+Fcahv9wZh5qAzv8CEpjW1hhTSrKJFVBAW7YlTlxvXCFBljnHS+tCg9UsLzLdf/XR/8+wrIiIiIqLSYCDlRKR5w46XB1ttKy4jlasvqCokGRZj4GFo/Odyagf7qPVO3h7V4WcIpKRFuqyBOhSfho9vba+2vTWyDZbvj8djAxub9wszZK6kE6C0grcNpHQShO2OTWEgRUREREQOYSDl5PRASm9vXlxGKiu3wCqb4+7mupGUZJXevbFtke1Syvf6iNZW2yTgkpORsQSwVpA3GkT4medSDW4Zifm7rAf6ymwpIiIiIiJHcI2UiwRSSem2pX2WjJTJZDI3n9BLAfWhuJcjY3e/WkE+GNQ80nz5rp71i+yflGH92hIRERERXcjl+2nbRXgVBkTxadalabmGibt5hvN6IPXStS3Vzwf6NsTlJsLQ3a9OiI/q5vf57R3x8S3t0a1BGAY0q6HKBns3Dlf7JGcykCIiIiIix7C0z8npmaXtJ5OKzUjlGs57uGslfX2aRGDbi1ci0DDQ9nLRMMJfDQJOSM1Gp3ohatuVLWuar//ijk5qjdmXK45gxYEEJGXkVOLREhEREZEruvw+ZbtoaV9CWk6xa6SMHfqMJX3GeUyXk+rVq+Hn+7qptVbFXe9V3Q3Bvtrrw9I+IiIiInIUAykXCaRsGbv2GYfzuhtmUF3OiguijIJ9PMs8kJJM4cTftqvzb9/Q5rJeq0ZERERUlfFTnosGUlYZKb3RhHv1UgUQpDFnpDLLrrRv/ZFz+GPLKXVayWG/RERERFUWAyknpzePsGVcF2UOpJj9cIhe+rj/bBq2HD9/yfcnM6ke/Xmr+fKe2BS1jYiIiIiqHn7ydtWMlOEDuh5Iebjw7KjKUCPQ0t3v0yUHS3Wbe7/biKs+WI5EQxdFGZb80/rj2Hj0HOINg3/fnrcPgz9Yjqzc/DI+ciIiIiKqbAyknFx2niXzZFwDZezal1PYbILrcRxTI8Ab/ZpGqPNbTySpeVwlSc3KxcLdZ7H3TCqmLTuE9MIhyd+uPoqnZ+3AqM/XFrnNwbg0DPjfMjzy4xarLCIRERERuTZ+8nZy2TbZDH3YbK6dNVIMpBw37baOqiRSuiIeS8wocd+zKVnm81+sOIJrP16pyvd+3XiixNudSsrEnG2nsfHopZcPEhEREZFz4CdvJze6a4zV5VA/rRwtz9C1z9hsghzj7eGGttFB6vzaw4kl7puapWWgdIcT0jHkwxWlHuh7LDH9Eo6UiIiIiJwJP3k7ubphvph5b1fz5fDCjJSxa5/e/pxrpC5OtwZh6ueaCwRSWbn2S/PO27RPD/e3rL0yOptiWT9FRERERK6NgZQLiA7xNZ9vGOFvp7SPa6QuRY+G4ernhUrvSts0ItLQxMIoLtVSGlgaOXkFiE3OdOg2RERERFQx+MnbBUSH+uLe3vXx7NXNEODtXrS0r7AhBQOpi9Mgwk/9PJOSZdXE4+IDKW+72+MMHf1KY/Lsnejxf/9hyd44h25HREREROWPn7xdxHNDW+C+Pg3hXr16sc0mOEfq4kgpnnRDlJlPJQU7WXlaINW7sZbBKm1GqkaAdjkpI8ehbNTPG09AGgl+vvxwqW9HRERERBWDn7xdjLubnfbn+hopd66Ruhhu1auhZpCWRSqplC4zR3udvdzd0DEmpNj9IgIsGak7e9TDeze1U+f3xqbi5s/XoN7T/+CrlUeKvb20Yv98+SHz5cLYmYiIiIicCD+iuRi9oYRxIK9kL7Tr+M95sfQGEdIG/UKlfT6ebvjolvYoHOlVYkZK9g300coxU7PzsPbwOXX+jbl7rG4j2bB35+/Dsv3xGP7pKry7YL/5usQSjomIiIiIKgc/ebsYS2mfsf05m01cqjA/rRviufScC5b2ebtXR+1gH9zbp4Hd/eQ6nUf1agj09iiyjwROxgHAP64/jk+WHMSYr9cX2TchLdvqfM//+w/PzNpR6udGRERERGWPn7xdNCMlH8R1XCN16UJLE0jlFAZSHm7qp6+HlmmyFROmNa8Q1apVMzcIsWV8rN2xKcU+ruyn/3v/tP64GvArgZfxPUBEREREFcv+JzxyWu5uxTeb4BypixdaOJ/LmP2xlVVYQinlesK38KeehWoS6Y9AHw/UC7O0q5esU4CdjJSITc5CWGFJYVwJM6YkXpJgKiLACyfOWdZwSfMKeby3/t2LmDBf3N69ngPPmIiIiIguBQMpFyPd5URGTp6dgbzMSJVraV+upbRP/TQEUuEBXph+V5cit5F9PN2rq4YWegapbqgvjp/LUE0l6of7wcfDDYv2nC3x+CTAk0Bq28kk87akzFysPJiALwsbV4zoUAf+XvxfmoiIiKgi8JO3i9GDpdWHEtUHcZGbV7hGqvADPjkuzM+r9IFUYQDl72UJpIJ8rLNOH9/SHle2iMTorjHqcoFhPVSTyAD18/nZO3H1RyswffXRYh8zxFe730m/bVeZx/1nU83XJWXkYu3hRPPlY4nppX6+RERERHRp+MnbRdufixf+3Kl+co1U2ZX2ldQhLzNXe5293bUAKsDLo9hA6tq2Ufj8jk7m7YY4CtGhlmYUxxIz8Ne208U+5pUtaqqfO04lY/GeOFXmp0vOzLEq9Yt3cOAvEREREV08fvJ20a59xrbnXCNVwaV9hc0mjE0kIgrXOpVGv6Y1rC7rmUV7gfBrI1qZW7O/NW+v1XWSkTLOvWKbdCIiIqKKw0DKxRiDJb3Mj2ukyrZrn7Etuf05Utrr7G8IpIyzoy6kXXSw3e1to4OsLo/uWlf9m356a3t1+UiCdenehF+24VC8ZVtJQSARERERlS1+8nbRrn1CmhRYZ6T4z3mxpJGDHpQWF5CkZedZve7G+VCRgd6lfizbMkBRrRrQqrYlkLqlS128PqK1Ot8xJqTY4b9GCeks7SMiIiKqKPzk7WL0D/HqfGHTA73ZhHSHo4vj5e6GGoXB1OmkLKvrJFC9+5sN2HJcK8HzL1wbZVXaV3jb4vRqFK5+9mkSoX6O69fQ6vqagd7qpPMzdASU4Fkv7yvJOZb2EREREVUYfvJ2McayMD0DxWYTZSMqWGsCIQNvjf7bG4fFe+PMl/UAythqvEWtwBLv++0b2uDuXvXxzJBm6vJTVzXDhEFNzNdHh/gipLC80Bgk60J8LdcVF1QlsrSPiIiIqMLwk7eLkazT2yPbWK2NsqyRYrOJSyFDdcVpm0Bq1+kUq8thhR3+JFM095He+OuhXlZBUHFB2uRrWqC5IeAyrquqHeJjFSzZBlLGcsCGEX7lHkhJIxN97pWtTcfOo/Pri/DOfOvmF0RERESXEwZSLsi3cH5RdmHzA/MaKZb2XZJaQVppnbETnjh13nL5sYGNUSfE13y5RVQgWtexbhJRWtGhlvupEehlFSwZSzhFoOE6eUwjPUO2/0wqnvhlG1YdTMClSM7IxeAPlmPQ+8vMnSGNvl9zVLVa/3TJIXMDDiIiIqLLDT95u+h6HmMmKje/cCAvS/suiZ5VSs7Mtdp+KilD/fxgVDs8NtBSjnepOtcLNZ93q1YNwYXDd+0FUsYga1TnaNXRT9ZbNa7hj9eGt1LbM3Pz8fvmkxj95Trz87hz+np8sfzwBY9l0m/bcOO01aqhxrTlh1SHwMPx6aqscc0hy9BfcS7D8vocjEtTgdUrf+02B/RERERElwPLIg9yGXpTiezCAbF6VsCLGalLoq950rvz6eIKB9060pmvNCTwfXV4K/yw5hhu7x6D6tK6r5CXh/W/pbFsUzJiekc/e8crJJP026aTWLovXp3u6BFjDsBtJWXk4JeNJ9X51QcTsPKAJaP1wA+b1M/3bmqL6zvUMe+vW3/kHF75e7c637leCIa0rmV134lp2Xj4xy0Y0DxSrREjIiIiqir4ydsF6QFTdp4WQGXk5NvNYpBj9BK51CzrwCSz8PU1NpcoK7d3i8H8x/ugVpCPVdbJGFQZ/43tHYd0+HO36Y+ekJaNuNQsu+WJtlYdTLQKGnfHWq8JExKU6c4bAik9iBKHbeZciZ82nMDqQ4l49e/dLAMkIiKiKoWBlEsHUgVWH/R9PZlgLI+MlJTMGQfxlhdvQyBsbIWul/OJDnWLDvOtVq0aGtg0oJA1TGeTLYHU2RT7M6aOJ2Zg/MzNlsvnMuw2mTA2wjifbl36qLNdWyYOxaWZz9sOFCYiIiJyZfzk7YL0Ei1zIGX+oM+M1KXwL8xIpdlkpMwZvwoIVF+5riWOJmSgS33L+inRs1G46g4YHepTzO1a4clft+FkYebppw3Hsf1ksvl62yHDBQUmLNpzFltOaLOxdCfPa+vBbBWYTOaSQXulhOJMctFg7ZAheDqTnGXVtZCIiIjIlTGQckH6+hnp2mcymcxrVsqj9OxyEujtUaS0T7Izeue6iiidvKN7vWKvK6k7YLcGYVj5VH81OFhmXv24/oTV9VKO98D3m1SDkq/GdMLPG0/gmVk7ityPHojZSi8MJpMyi2+xLoGZZEf1gF7em4cNGamzKdaDjomIiIhcGUv7XJA+eFcyUjI7KCUrD7KkJibM0k6byqa0T8/2ucoatJgw+zOmTpzPwLxdZ1QXPhk4LOuW7NEDqbbRwZg4uKm5k2BmTh5Ss3JxzUcri9zG29AY47k/dliVF6YaXsszDKSIiIioCmEg5YL0Nud5BZKNyjVnU4xrbOji53Nl5OSpbIpx/ZltwOCsrmlby9zV0ehIfLpVQ4niRjfrJYAhvh4Yf0Uj1fJdL2+cvfW0uYOhUa9GEebzs7acUq+dZPH2nkm12k8yUi/8uRPDP12lZlURERERubJK/WT45ptvonPnzggICECNGjUwfPhw7Nu3z2qfrKwsjB8/HmFhYfD398fIkSNx9uxZq32OHz+OoUOHwtfXV93PxIkTkZdnfx1HVeBe2Apbys6SC0utAn1Y1nep9GYd0mvBtpGHZKOkqYOz61A3BOueGYDlE69AF8OcKmOnvYzs/CJrpiIDvawuhxY2l9BfE3kdJCtl9zFjgjGweQ2rluktXpiHO75eb7Xf/rNp+G7NMWw9kYR/dsSW+DwkGPtl4wnsP2sdjBERERE5i0oNpJYtW6aCpLVr12LhwoXIzc3FlVdeifR0y7fnjz/+OP766y/8+uuvav/Tp0/j+uuvN1+fn5+vgqicnBysXr0a3377Lb755hu88MILqKqMg3fPFXZQ8/eytM6mi2Ms3UsvLEnTS/t8XaiRhwwWrhvmi18e6I4H+jYsMmR4zeEErDxomRVlr0tgsDmQ0rN0EnzZzyJJA4mPb+lgvjx/11mVLdV1a6AFdHsMbdWf/WMH/tx6qtjnICWIk37bjivfX27ODhIRERE5k1KlMYyBS2lNmzZNZYdKMm/ePKvLEgDJbTZt2oQ+ffogOTkZX331FWbOnIn+/furfaZPn47mzZur4Ktbt25YsGABdu/ejUWLFiEyMhLt2rXDq6++iqeeegovvfQSPD0tbZurCuNwVilDE/bKucgxbtWrqfK9rNwCFTiEGQIpVy2b1MsRUzIt2aRPlxwqsl8NFUhZuvzpAZTeOELeZ8ZBvAFe7gjy9VAleu3qBKv9pC2/nskzmnRVM1w/ZbXVLCzx6E9bMaxtlN1M3/qj58zn5d+Arf2JiIjI2ZTq0/fs2bNVQBIUFFSq0z///IO0NEu3rtKSwEmEhmrfYEtAJVmqgQMHmvdp1qwZ6tatizVr1qjL8rN169YqiNINHjwYKSkp2LVrl93Hyc7OVtcbT67Evbrln00vPfOwGchKF8ev8AO7/qFfD1RdKSNlL8tmzEjZY5uRalPYIVB/3hLMGO/jizGd8PfDvbD4ib4qAyZCC38ajb+iIWJCi2+CUtx8q7x8SxbKtgyRiIiIyBmU+mvejz766IIZJt1vv/3m8IEUFBTgscceQ8+ePdGqVSu17cyZMyqACw62HkIqQZNcp+9jDKL06/Xrilub9fLLL6MqZKT0jIm+boouveFEYrq0+9YCqCwXn9GlZ9KM3QftsV0jNaC59v+Qr4f2KyI334SENC3o+fDmdqrdur2hvbGGIcCiQbi/2l5ctupAXCpqBnmr8r2X5mhffLx4bUurNV0yALhOSKmfMhEREZHzZKSWLFlizhKVxr///ovatWs7dCCyVmrnzp346aefUN6eeeYZlf3STydOWM/ccXZSCuVemIHSPyAb103RxdMDBz3Tp2emXLW0r7Qt27XSPs29veurMkfbAFIPkoJ87K/Hs5eRahDhh+rVq6GzofHFlS0izc0pjiVqA4APJ6Tj2zXH1GntkURzN0px6xdrcUyiWyIiIiInUqpP33379oW7e+nXKPTq1QteXtbfcJfkoYcewt9//60Ctjp16pi316xZUzWRSEpKstpfuvbJdfo+tl389Mv6Prbk2AIDA61OrkbPQGUVftDXAysqmxbo5mYTha+vq5b26cObL8RY2hdQOJhYX3unv7f0QEpvRGFLL/HTdYwJQYso7f+tJpEB5u0SiEUXlvsdP6cFUluPW/4f33j0vFVGSmZRXf3hCrVG6+T5jGKbTxQUmPD6P7vx4p87kZdfNPtFREREVJYuOo0RFxenMkjbt2+3OjlCPhBJEPXHH3/gv//+Q/369a2u79ixIzw8PLB48WLzNmmPLu3Ou3fvri7Lzx07dqjj0UkHQAmOWrRogapKz0BZSvuYkSoLxi51xtfXFYbx2lNcJq12sA+eH9rcfDkq2Md83ja7qWelpN2+CC4mIyWzp3S3dInG7+N6wMtdu21EgJdVIKWvm9IzTdISXffewv3Ydcp63WJ6Tj7avbIQvd5aolqoy3Dg44kZ2H4yyXxc204m4YsVR1RWa/Fey++DlKxcc2BMREREVFYcboUlDSDGjBmDPXv2mL8ZllIzOS8/pR25I+V80pHvzz//VLOk9DVN0rDCx8dH/bz77rsxYcIEVVoowdHDDz+sgifp2CekXboETLfffjvefvttdR/PP/+8um9HsmKuHkgZ103RxdO7w5kDKX2OlKtmpIrp5ji0TS14GYKsOiHGQKpakeAyNcsSiBRX2idDoXV+Nl32wv09rQOpMD+r0r7tpywdA0VOCRmlN+buwazNJ7HtZLK5VPDzOzrhwFlLgxsJsLrWD8VPG07g/YX7Ee7vhXmP9bbKthERERFVaCA1duxYNGnSRLUll6YOlzKkdOrUqepnv379rLZLi/M777xTnX///fdRvXp1NYhXuu1JR74pU6aY93Vzc1NlgePGjVMBlp+fnwr0XnnlFVRlermV/oHf2MmPLp6fod13VchIeRaTqZRMVYe6WhOXuqG+VpmrIhkpm+ceWEwgZQyw/L2tf7UYM1Jye5lzJfaeScWmY+dx6rwWUJWGNK3QgyixYPdZFTgdircEUsfPZeKZWTvw707ty5lTSZlYtOcsRrS3lA4TERERVWggdfjwYfz+++9o1KgRLlVpBm16e3vj008/VafixMTEYO7cubic6B929a5y7NpXNnwKMynp2flVYo2URzEZKQmOWkYFYe4jvc1Bja5V7UC7r4kI9HY3N6KwFehj2c/fyzYjZV3aJxkw+Q5GfgWMnLrafN0Ho9rhsZ+3wlFTlx5CriGLdTY5y2oWlfhs2WEMbB7JrBQRERGVCYfTGAMGDMC2bdvK5tHpounlV/oHfTabKBt69iUrL79KZ6T0wFCaQehBz7+P9sa02zqgY4x1h05jEClDeItjzEj52QRSNQwZqQBvd7V2yvZ7FGlsIQ0qSmt4uyg1y0od+84zWLTHsi7KGER1qRcK+d9Dsl9fLD9s975kndW3q49i9cGEUj8+ERERXd4czkh9+eWXqnROGk3IvCdpBmE0bNiwsjw+KobeXILNJsqWfJgXOYUzj8ztz101I1VsaV/R7c1rBaqTLWMgFexjv2Of7Rop24yUtEYP8/NEYnoOmta0dPCz7RxobHph69audXE0IR2rDyWqy81qBaJlVKAKlGyzT7r+zWrg6zs745cNJzDp9+2qDHDClU2L7Ddn2ym8OGeX+vff9PxAZq2IiIio7AOpNWvWYNWqVWpWlC1Hm01QWTSb0D7wezAjVabNGbJtMlK+rpqRcrf/vnBkLpYxGxdcQkbKuHbKNpCSQP+3cT2QX1CAOiFaKeEzQ5rhzX/3mveRtVpSNjjpqqZ4e94+3N4tBvf1aYB35u/Dgt1nMK5vQ9U4Qtc0MkD9znnrhja44t2lalu9MF8cLWxgIR7qr5UgDyicWyVZqfjUbHy96ogK2m7rWlfdx+ZjSeYAWvaRgPK/vXHo1zTCKkAkIiIiuuhASrrm3XbbbZg8ebJqNkGVW9pnniPFjFS5ZKRcvWtf8Rmp0j8fY5lecY0mbEv7jOuldPXDtU59OgmSDsal4ddNJ9Vlfa3Wg/0aoU/jCDSM8Fev+4c3t0NWboE6bxz626QwsyX3e/T/hqoAScoGm02epx23pzTU0EoFw/y91HDgw/HpmPDLVqw4oJXwSRv2Pk0iEJucab7fxLQcvDl3D2asO666G356a4dSv1ZERER0+XD403diYiIef/xxBlGVTF8TZSntY0aqbDNStoGUw985OFVgaMuRNV/Rhtboxc2QEsbMTZjfhUcPSCbIuCZKny0lWtUOMgevsp9+3lhmGBVkGSKsdwY0Boi2TTG6NwhTP/UgSuhlghKE6RLTszFn62l1/p/tsWoGld7FkYiIiOiiA6nrr78eS5YscfRmVMb0DJS+hseD7c/LNJAyr5Fy8WYTZZGRaljDv5Slfe5oXzdYrVuKNgRFJZFMkS7GpnugPXf0qKfmQ00Y1OSCoxdss7TXd6hdZJ8T57QywIS0HPO2U+czzYG0aPnifPR5ewniUrLMZZ+v/r0b01cdKVXnUSIiIqqaHP6aXWZIPfPMM1i5ciVat25dpNnEI488UpbHRxfISOlreZiRKp/SvixXb3/uQLOJ4jQyBFLFDeMVEtj8/kAPdb56KdfsGUv16oZal/7ZI23Uf76/e4n7SHmfDBDu2yTCaruU+cnsrM3HtfVQIi41C2eSs3DaUNo3ZemhIvcpgdbvm09hXL+G+GtbLL5aecRcVtivqbb+ykiaYvh6uaFGgHXWjIiIiC7zrn3+/v5YtmyZOtl+kGIgVTH0siX9m/PiPjCTY6Qtt1VpX2FGypEMjqsM5C0tWaukq36BLFBpAyhdg3A/FaRKJtAYsF2KH+7uij+2nMKEK5sU+f307o1tMfabDSgwydDeDGw4eh7d3lxcqvv9c6sWSM3fpQ35FasOJqBtnWDkFZjMQ4e3nUhSs7EkSFz8RF92ACQiIqqiHA6kjhzRvomlyqWXNemZE86RKt/2565a2lfcGinvwoCxNCTokucvQWX3hto6o7IS4ueJRRP6qvsv7lgd1TY6WJ3saRDhj6UTr8CRhHRzp7+SLH2yH9Ky8zBiyirVzW/HyWQs3x9vvl46+0mmKi+/AH8+1EtlqH5cf1wFVnGp2Wj90gJc3bomPhjVvsyeHxERETmHS/rLLusDuEagcthW8tkurKeyaX+elevapX3yvrD31vD2dOx//YUT+uDn+7qhZVQQypq0IZeAqiLp2aOSvDa8FeqF+6nGF/rzvve7jVbrpw7Fp+Nceg5SsvIwd0eset/IT6O5O87gw8WWtu1ERER0GQdS3333nVof5ePjo05t2rTB999/X/ZHR8WyDZxY2lc29KyBfFiWLwn0bm2u2v68uPeGo6WKMvupa2HXu6pAWqOX5H83tsXornXNl2U+lThT2HDi7RvaFFlnJiV9m46dV0FVjQAvvDWytTmIlXVXywozWYfi0/D58kNIyrA0uCAiIiLX4/Cn7/feew/jxo3D1VdfjV9++UWdrrrqKjzwwAN4//33y+coqQjbtSqOrk2hktdISWlfTn6BWkvj6oGUvXVSjpT2VUW2Hf9a1Q7EN3d1RotagejZKAwj2te22qdumHUjjKtb18KAZtYjILadTDJ3AZSBvqM618XhN4fixo51IIn75/7YobJX437YhDfm7sUbc/eU63MkIiIiJ1sj9fHHH2Pq1Km44447zNuGDRuGli1b4qWXXlIzpqjiM1JuF2gCQI5npPQZUq68Rkp4ebghNTvPXLoos5v0gc6k+fvh3uqndOCTTKRtoFUn2McqO+Xv5Y4xPerhnx2xKruVnpOPsynZmLbssLa/YfbWy9e1VLOrTp7PRIdXF5q3/7LxJJpEBuCe3g3U5U3HziHIx7PMmm4QERGRkwVSsbGx6NFDa3FsJNvkOqoYthkoJqTKhh5gSDbKPOy4ejWXLp2UduAJadrA2YWP90V0qM8FZzBdDgK83FWA2a1BqNV2e69NrWBLG3N9vVSX+qGY9WAPRPh74bGft6qyPmlioZdC6nw93TGkdU1MX3W0yP2+t3A/7uheDy//tQsz1h1X5YLv39ROtW//bdNJFdg/MqCxeiwiIiJyLg5/OmzUqJEq57P1888/o3HjxmV1XHQBthkolvaVDT1gki5srt6xT6d/uBdh/p4Mogr9cE9XVXY3ZXTHUjXE0DWtGWA1m0qGD793U1ur/Y0ZKdG8ZmCR6yWrLO+x79YcVUGUyMotwLgZmzHp9+1Yf/QcVh5MwE2frcFtX67DigOWboFERETkghmpl19+GaNGjcLy5cvRs2dPtW3VqlVYvHix3QCLygdL+8qH3kY+L9+E9MJyOH9vh/83cSoxYb44lqit3fHzcu3nUpZKapNuq26or8oWSaDT1U52SDJQshZNMpl2A6la1oHUq8Nb4X8L9mHnqRS89s+eC/7bSUC1JzYFa54ZwDbqRERETsLhv8gjR47E+vXrER4ejtmzZ6uTnJdtI0aMKJ+jpFI0m6i0Q6mSGancggKkZOaZS+Nc2Xdju+CqljXx+7iiJblU+vfFbw/0wBd3dLLbvVC+2DC2VDeW9unNLB7o2xD39q6PPa9chSua1kD9cOu1UH8/3AvdDff9wah2mHZbRzSM0BpdJKbnYMm+uHJ4dkRERHQxHPqEmJubi/vvvx+TJ0/GDz/8cFEPSGXDtpLPNrCiSy3tMyE1K1edD/T2gCuLCfPDtNsvXL5GJZN5UnIqjruhgUe4v/VcLCmnfHpIM6ttDcItnQBv6RKt7vvH+7qpZhcSxAf5au+7xU/0w5tz9+Cz5YfVuqnBLWuq7RuOnlPdF1vXKfvZXkRERHRhDuUxPDw88PvvvztyE6qg0j4GUmVD/zCcV2BCSmEg5eoZKaoYUv4npPSuNOvQGhRmmkT3huHm83JbPYjS3dCxjvq5ZG+caqE+f9cZ3DhtDUZMWaVK/oiIiKjiOVwQNnz4cFXOR5XLtrmEbWBFF8fDUCN5PqMwI+Xj2hkpqhgvD2upOgBKSV5pdG8YhiAfD1US2LdxRIn7No4MQOMa/irA/3TJQdz//Sa1XS7P3nqqTI6fiIiIHOPwV+3Sme+VV15RDSY6duwIPz/rQZWPPPKIo3dJZdG1jxmpMmEszzqfnqN+MiNFpdEgwh8/3de91PvXCPDGqqf7qwYn3qXoDCklfAfi0vDVyiNW2+fvPIOnr2pmzoIVFJjYxZOIiKgCOPwJ8auvvkJwcDA2bdqkTkbyh5yBVGWV9lXaoVTZQCo5U8tI+XkykKLyIYN9S6tVVBBmbbZkn6aO7qDmVx1NzMCOU8loUycYO08l446v16N5rQB8N7YrM9VERETlyOFPiEeOWH8bSpXDNgPFD0xlX9qXpQ/kNQRXRJXF2FRCygGvalUTg3ZE4u/tsWrNlARSz/2xQ62hWnUwUQ0I1gf5bj5+HjGhvgjzt3QWJCIiogpeIyVlfRkZ2kwao8zMTHUdVYzC5nJmLOUpG/I66i9lZmEg5cbe8uQEjLOomtUMUBUAeiv2vbGpiE3OxLaTyeZ9ZJBvn7eX4NYv1uL6KavR8bVF+HH9cSRl5ODp37dj1GdrMHPdcdUlkIiIiBxX/WIG8qalpRXZLsGVXEcVwzZw4hqpsuNeGKXK8FV1mUEqOUkZYO/GWne/0V1jrAb/nkrKxKG49CK3OX4uA6sPJZovPz97pyoH/GnDCaw7cg7P/rED/+yIrbDnQEREdFmX9sm3l/Za+27btg2hoVoZCVV8swnby3TxPKpXQ46htI9lk+QspozuoIKmZjW17FREYane+YwclZHSg6u4lGyYYEJuvsk81yohLQf5BSYs3RdvdZ8Ldp3FNW2ikJyRqwZ7B7j43DQiIiKnC6RCQkJUACWnJk2aWAVT+fn5Kkv1wAMPlNdxkg3bDBSrz8o6I5WPrDxmpMi5SJDTrKYl0NGHRcsA3zPJWep8z4bhGNevofodIQ1TZP3UXT3rqaBqyIfLVVv/emG+eG14a9z21TrM2XYaA5rXwOTZO+Hl4YZ5j/YuspZK1ltJEBbm74nMnPwSBxMTERFdLkodSH3wwQcqGzV27FhVwhcUZPlD6unpiXr16qF799K3/qVLw9K+8uNR2FwimxkpcnJ6a35Zz3e6MJCqEeiFeuF+dptU/HBPV2w+dh7D2tWGn6ebun1qVh4e/WmrtkNWHib9th1TbusAL3etJfvnyw/hjbl7zfchv2p+vb87OtVjBQIREV3eSh1IjRkzRv2sX78+evbsCXd3toR2qtI+ftgvM+6F6T1z1z6+tuSk/A0zzs6mZF1w7lnLqCB10r0xojUe/nGL1T6L98Zh+qqjeKBvQ8SlZOHtefusrpfeFDPXH2cgRURElz2HC8ICAgKwZ88e8+U///wTw4cPx7PPPoucHG2AKVVC1z5mpMqMh3s1q2YTbrYvNpGT8HCrbs6gxqdmq59+DsymurZtFLoUBkSyjur1Ea3U+f/7d6/q6LfyYALyCkxoGRWIJ69sYm52IfOsrnh3KR6auRnfrzlaDs+MiIjI+Tn8CfH+++/H/v371fnDhw9j1KhR8PX1xa+//opJkyaVxzFSqUr7Ku1Qquwsqaw8ZqTI+Xl7uFkFUo4M+RWvDG+JIa1qYsY93XBTp2iE+Xmq7dLRTwIq0bNROB7q3xjf3NUFwb7auqwjCelqhtXkP3epQcBERESXG4cDKQmi2rVrp85L8NS3b1/MnDkT33zzDX7//ffyOEayg6V95UcfwMuufeQKfD0LA6m0iwukpAPg1Ns6omnNAJXheuHaFubr4gqDs66Fg33l/4XXh7cuch/XfLwS/2yPxeI9Z7F0X9wlPR8iIqIq3f68oEAreVq0aBGuueYadT46OhoJCQllf4Rkl+2He5b2lccaKXbtI+fn6ym/xrNVVz1HS/vsua5dbQxoHokury9CRo72ZUKXwkBKDG1TCwHeXXDH1+utbjd+5mbz+XdvbIuT5zPU/zv39G5gzpoRERFVJQ7/xe3UqRNee+01DBw4EMuWLcPUqVPV9iNHjiAyMrI8jpFK0/6cgVSZ0dec6JiRImdmG6Q4mpGyR+7jpWEtVQe/O3vUKzJbqnvDMFzVsibCAzzh7e6Gr1cdQWEcpzz56zarcQLSuIKIiKiqcfgvrrRBHz16NGbPno3nnnsOjRo1Utt/++039OjRozyOkeyw/XDPD/tlPUfKcJlDusgFSvt0l5qR0sl6qUHNI81rooykBHDa7R3Nl5+/pgXSsvPw6ZKDmLr0kNW+qw4mMJAiIqIqyeG/uG3atMGOHTuKbH/nnXfg5sbyjcpqNsHGcmXHtpSPQSq5UiBVFhkpXUhh44nSkMcd1jbKHEgFeLkjNTsP646cw3sL96ND3WD0a1qjzI6NiIiospXZx29vb294eBT95pLKh+1n+2os7Ssz8m27EddI0eVW2nex6hsGAXdrGKZ+5uQV4KPFB3D/95uQnp1XacdGRERU6YFUSEgIQkNDi5zCwsJQu3Zt1cVv+vTpZX6gdIGufQykyrxrn87N5jKRs2akJHvq7VG9UoO6qCBvdf7WrnWtrsvOK8CLc3ap+VNzd8QWex8SbA3/dBVavzgfyZm55X7MREREF8vhv7gvvPACqlevjqFDh+Lll19WJzkv28aPH48mTZpg3Lhx+OKLLy76oOhi5kjxw35ZsV0TxYwUuUog5efpVunZ6Rn3dsP3d3fBFXbK+H7bdFLNn3pwxmZ8v/aY2nbiXAbeX7gfcalZ6rJs33oiSZUFPvXb9go/fiIiotJyuAZk5cqVqmvfAw88YLX9s88+w4IFC9QsKVlH9dFHH+Hee+919O6plGwzUOyHUHbYtY9ctbTPtrteZZX36SV+749qq9ZHZebkIyEtx2q/DxcdwI0d6+Dubzdg/9k0lX26unUt8xBgsf7ouQo/fiIiotJy+OP3/PnzVetzWwMGDFDXiauvvhqHDx929K7JAezaV37YtY9cNiPl5VwNf0a0r4MVk/pjYHPLaIwmkf6oFeSNhLRsLNx9VgVRQs5Lhz+jc+k5SCwcNExERORsHP6EKOuh/vrrryLbZZtcJ9LT0xEQEFA2R0h22VbvsLSv7HgwSCWXG8ir8TGcdyaRgdq6KdEuOhgdYkLU+Q8W7Tdvl19hO08lq/PdG4ShdrCPOv/rppO459uNWH3I/sD3pIwcbDx6Tg2Ln77qCN6Zv9c8nJiIiKg8OfxXd/LkyWoN1JIlS9ClSxe1bcOGDZg7dy6mTZumLi9cuFA1naDyw4G85cc2cOIaKXKZrn0m5wwgJAOla14rEGdStPVQh+LTzdtPns9UJ/Hk4Cb4euVRnErKNJf6HT+XjgWPW/9dycrNx1UfrFD3N6Z7DL5do627alsnGFe2rFkhz42IiC5fDmekZN3TsmXL4Ofnh1mzZqmTr6+v2nb33XerfZ544gn8/PPP5XG8VIilfRXXtc/2MpGzlvZd1642nFFNQyDVolYg6oT4lrh/i1pB6NZAq3DQSQlgv3eWYPn+ePM2KQXUgzI9iNLLBImIiMrbRdWB9OzZU52o8tjGTYyjyo5tdo9rpMiZSVZGN7qbdctxZ9Ewwl+V7knCrGXtIGTkWI55YPMa2H4yGXGp2lqoQS0i4ePphhs7ReOrlUdwNDHDvK+cf/2fPejTJEJdNgZVRkv2xaGgwFSkuykREVGlB1IFBQU4ePAg4uLkj1WB1XV9+vQpq2MjR0r7+IGhzNiW8jHbR87sfLqlG56Xu3M1m9BFh/rip3u7ISLASw0Mrh2irX8S/ZrWQHxqtjmQmji4qblk8c/xvXA0MR2P/bxVtU0X+86mIje/QA3OXlBM5kk6BD7wwyZsO5mERwY0RqeYULV2qnFkAJ68sin/nyYiojLhcCC1du1a3HrrrTh27Jha3Gsk80vy8y3fNFLFBVIcyFt2bINSrpEiZ3ZFsxr46L+D5kG4zqprgzDzeb2RhGgSGQAvwzqvuqGWsr8gXw+09Q0uMpLgkR+3YPwVjRCbnKUyXRH+XuZATP5/zSswmYMsWWPVIMIf204kYdGeOHSsG4IuDUIxZ+tpXNOmFoJ9PdXfsuPnMhAV7KMCNCIionIJpGR+VKdOnfDPP/+gVq1alT788XJl+40qm02UHWakyJW0rxuCuY/0tgpOnJ2flzteuKYFUrJy0bleiFo3tf7IORUMWjXPKNQyKsjcJl38u/MM1h5ONAde8tz1QOqunvXwxYoj5n1Ts/JUEKWb+Ns2NeJAsmDzd53B93d3xUeLD+L9RftRI8AL9cL8VBarV+Pwcn4ViIjosgukDhw4gN9++w2NGjUqnyOii2t/zi9Ryy8jxWYT5ORaRAXC1YztVd98/vGBTVTWqXdjbe2TrWeGNFMtzcP8PTF91VG17XxGrvrZuIY/snItJeaju8ZYBVK29NuJFQcS8MuGE/hwsdaGXYIxOT31+3asfOoKflFIREQlcvjjd9euXdX6KKpcLO0rP8xIEVUsKeF7bmgLcxMJWzUCvfHRLe3x9JBmuKVLtNV1su7pof6N4OleHTd3jka9cD90KpxT1aymZZ6hlPHZM+n37ZCxU70bh2PabR3UNmm7LmuxjOXradl5amYVERHRRWekHn74YdXe/MyZM2jdujU8PDysrm/Tpo2jd0kXgaV95cc2KGXXPiLnIM003ry+DVYdTFRrmvSMVLcGYdj0/EDVyEJ8d3cX7DuTipSsPIz5er3adkuXuqpscM2hRAxpVRM5+QX4ztAy/bXhrRAT5od+TSOwdF+8mk8lwdlbI1ujR8NwXPfJKlWKOP+xPvhl4wlsOZ6EKbd1QKC39d9AIiK6fDgcSI0cOVL9HDt2rHmblD/IN3dsNlGZpX0MpMqKm03gxIwUkXNpWjPAHEhJswoRYAhofD3d1dqxvPwCjO5aVzWQ6N4gDD0bWdY9SRdAPZDqUDdYBVGibxMtkBI5eQV4/OdtVo896P1l5lLCv7fF4tauztlynoiInDCQOnKk+NpzqpysCT/oly3bpl3s2kfkXKRMT4buSjt1CaqKI00lXh/R2u519cP9VBD0z/ZYPDqwiXm7bHv5r93F3qdxPdau08nmoEzmee09k4J20SHqvomIqOpzOJCKiYmxu13mSc2dO7fY66lsGTNQ/JxftpiRInJu17WrrTJRQT4el9Su/I0RrdXJtnxwx0tXqmHAMjj4m9VHVWbKnj2xKWoo8H3fb7QKsL6/u0uxjTOIiOgyH8hrJI0nvv76a3zzzTeIj49Hbq6lIxKVH+OaKK6PKlvMSBE5v+a1yq9ToZQJPlaYpZKOgZKhSkjLVrOwJs/ead5v8/Ek3FG4Bsvo9q/W4++He6FV7SDEpWSpWVWy3oqIiKqWiwqkMjMz8euvv+LLL7/EqlWr0Lt3b7zwwgsYMWJE2R8h2WX8bM9Aqpw7IjKQIrpsydrfl4a1NF+WWVfS7OLrVdZl7rWCvDH5mhZ4cMZmdXnZ/nh1emf+PkQGeuGX+7ub12EREVHV4NBXZBs2bMD999+PmjVr4oMPPsB1112n/shMmTJFDeqNjIwsvyOlYj/s84N+2TJmoOS15SwZItINaB6JF65toZpY+BiGB9/dqz6ubl0LEwZpmazD8en4deMJdf5sSjY+XXIQG46eq7TjJiKiSgykpK35jTfeiLCwMKxevRqbN29WbdD5IbNyGIMnxlHl99oySCUie6SJxe5XBqN/sxrw83TD4JY11fYGEVrW6WB8Gk6ezzTv/8vGk7hx2hrVOp2IiC6z0r59+/Zh1KhRuOKKK9CiRYvyPSq6IGP8ytbn5ddsguujiKg48kXiV2M6qZlU0qRC1AryUT93nExSg35tvf7PHgxrG6VmWhER0WWSkTp8+DCaNm2KcePGoU6dOnjyySexZcsWZqScIWvCf4NyazbBjBQRlUT+BupBlAj391Q/9SBKWqF3qR9qvj45M9dc8ueI5IxcHDibqmY2EhGRiwVStWvXxnPPPae69H3//fc4c+YMevbsiby8PNWxb//+/eV7pFR81z5+2C9TzEgR0cUK9dMCKV10qC++HNMJM+/pqppRiJnrT6DAXrqqGIfi09D/f0sx6P3l+GjxwSLX7zuTiqSMnDI4eiIicsRF9WPt378/fvjhB8TGxuKTTz7Bf//9h2bNmql1VFQZ7c8r9VCqeEaKLYuJqPT8vdytWp3XDfVBoLcHejQKx7Vta6kvZ2T+1AeLLvzl485TyXjz3z2477uNSEzXAqVpyw7hxLkMlZlKzcrFS3N2YfAHy3HtJyuRl29/3hUREZWPUn9KzMjIKLItKCgIDz74IDZu3KiaT/Tr18+hB1++fDmuvfZaREVFqfKI2bNnW11/5513qu3G01VXXWW1z7lz5zB69GgEBgYiODgYd999N9LS0lDVGYMnlvaVLWakiOhiyd+pMENWKjrE13y+RoC36vgnvlhxBNl5+cXejwRKE37Zis+WHcah+HS1TQYQZ+bmo/fbS9Dihflo/dICNTBYnDiXqe5zzrbT5fjsiIjoogKp8PBwXHPNNfj8889VWZ+tdu3a4aOPPoIj0tPT0bZtW3z66afF7iOBk2S+9NOPP/5odb0EUbt27cLChQvx999/q+DsvvvuQ1XH0r7yYwxMuUaKiBwVVrhOStQNtQRS4vZuMSrQkoBo+8nkEsv59p+1fCnYuIY//n20N8L9vdRlub3tlz1vzduLR37cgrumr8fVH67AxF+3IT07r0yfGxERXUQgtXfvXgwePBi//PIL6tWrh65du+L111/Hjh07cLGGDBmC1157rcRBvl5eXmpulX4KCQkxX7dnzx7MmzdPDQaW4+nVqxc+/vhj/PTTTzh9+vRl1P6cH/bL67V1d+NrS0SOCfPTgh19jZRtxqptdLA6v/9sqt3bH0/MwLRlh82XuzUIxTs3tkVUsA8e7NfQ6nfVa8Nb4YXCtVe6JfvisTs2Bb9uOokr3l2KzcfPl9lzIyKii2h/XrduXTz88MPqlJycjLlz5+LPP//EO++8g9DQUAwbNkyd+vbtCze3smvrunTpUtSoUUMFULI2SwIvmWUl1qxZo8r5OnXqZN5/4MCBqF69OtatW1dsgJadna1OupSUFLgaY+zErEnZ4hwpIroUxkG9dcOsAym1rTC4On6uaMl8Vm4+hk9ZhXOFa6Ie7t8IT1zZ1Hz9XT3rqUCsSaS/Wo8lgdnZlCxMXXYI8amWv2sebtWQm29CXGo2rp+yWu0/7baOqougbM8rKICvZ6k/AhARkR0XtZJe1kbdcsstKvMTHx+Pzz77DPn5+bjrrrsQERGBGTNmoCxIWd93332HxYsX46233sKyZctUFkseS0iJoQRZRu7u7iqws1d+qHvzzTfVc9BP0dHRcDUcyFt+jKUyHmw2QUQOkrlSOmk0UVwgdfKcZWCvbv6uM+YgSnSMsVRhCAmcZFuAt4d5/EhkoDfWPN0f2168Ep6F3XIkaJKgSydlgtdPXY12ryxEk+f/RcsX5+P9hZaGF2xUQUTkuEv+OsrDwwODBg1SJymrk9lS0hK9LNx8883m861bt1ZdARs2bKiyVAMGDLjo+33mmWcwYcIEq4yUqwVTxnI+Zk3KlnHNGV9bInLU+CsaYtXBBNzUyf7flegSMlK/bjxpPi9BUQebQKo47m7VEeRTHd+O7aIyVP2b1cCA5pF48dqW2HU6GUM/WomkjFzz/jKO6sPFB9C6dhD8vNxx5/T16FQvBFNv64gle+PQoW5IkbJEIiK6xECqoKBAlc7Z6zB04sQJtG/fHuWlQYMGqumFzLKSQErWTMXFxVntI0GcdPKT60padyWnqtP+nB/2yysjxTVSROSojjGh2PLCIKsSP6OYwnK/ownp6m+nnlmSoburDyWo808PaYb20cF2M1ol6d5QK303ahkVpLJgeuC24PE+mLH2GL5dcwz3fLfRvN+qg4lo89ICdb5moLdaf9W6TpDKeBERUVGlrluSrM1NN90EPz8/REZG4oUXXjCX2AkJaOrXr4/ydPLkSSQmJqJWrVrqcvfu3ZGUlIRNmzaZ95GZVhLsSfOJqsyYKNH/CFPZYLaPiC6VrD8q7nezBDXyqyU1Ow/xaZZ1TSsPJkDm9EqHvgf6NkTXBkWDoov1+KDG8HKvjomDm6JJZACeG9oCA5pZl8YbnUnJUkFW1zcWq2HAf249pYK+00mZ2HTsvEMDhYmIcLlnpCZPnoxt27bh+++/V8GLNH2Q2VGzZs2Cp6fW6lV+yTpC5j1Jdkl35MgRbN26Va1xktPLL7+MkSNHquzSoUOHMGnSJDRq1Eh1DxTNmzdX66juvfdeTJs2Dbm5uXjooYdUSaDMprpcPuzzo37ZMmahOEeKiMqat4cb6oRoGaJDcelqvpTYcUprh96tDAMo3Yj2dTC8XW1zcCdDg7+6szP+2HISu0+n4IpmNXDrF+vs3vZwfDoe/Wkrnp21A+k52heoV7euiZeHtUJEgGtXdxARVUggJcNyv/32W/PQ3eHDh2Po0KFqoO6cOXMuKjMig3yvuOIK82V93dKYMWMwdepUbN++XT2mBG4SGF155ZV49dVXrcrypLGFBE9S6iclhxJ4OTrPytXX8TAhVbaYkSKi8tasZoAKpGT9kl6OF5eSpX7WDvEpl8e09zdaAqwRhRX5b49sg+2nkvBw/8aYtfkU/LzcsCc2BesOn8PhhHRzECXm7jiDzceS8Mv93VUmTdZk1QzyRkZOnippZKUEEV0OSh1ISXe+mJgY82VZq7Ro0SKVHbr66qvVLCdHSVBWUhZr/vz5F7wPyVzNnDkTlxt+vq+gNVLs2kdE5UCaPCzYfVZlg3TSqlzUqKQsz02do9VJjDPMq5K/01tPJKnzzWsFYsPRc3j4xy2q/K/PO0vU9gbhfri+Q238b+F+dI4JxavDW6kSwH93xuLGTtEY1rZqV4kQ0eXJoTlSMgDXuA4qICAACxYsUJmikobqUtkzZkr4xV/Z4hwpIipv9cL91M+T5y0t0ONStYyUXurnLCS71L6upXtg78YRarbV5Nk7zdskY/XuAq2d+vqj5zD4g+Xm61YcSMDqgwno1Tgcg1pEwsu97GZNEhFVplJ/3S7B0vTp04ts9/f3V5kjb2/n+sVf1bFTX8W8tlwjRUTlISpY+5t5OtkSSJ1NKcxIBTr/uqNrWmtNn2yF+nkWmX0lftpwAg/N3ILmk+eh99v/Ye6OWIfXVRMRuWxGSho/nD592u51kplauHChaj5BldFsgh/2y6vZBDNSRFQeagVp66DOJGepDngyxDc5M7dSS/scEeLniRHta6tufjL8V7oUSinfLV2jVUZNAqVT5zMxpkc9rDmciClLDmLdkXOqK+GJc5l4cMZm+Hm6oVFkAG7vFoMbOtap7KdERFR+gVRISIg6FUeCqb59+zp+BFQG7c8r80iqeEaKc6SIqBzo3e7yCkxIycpFalaeuZtekI9js6Mqy9s3tMEzVzczlyJK6Z7uakPGqm+TCHV67KctmL3V8oWsNK/YdiJJnSQI23cmBQlpOcgvMOGunvVwW7cYZObkq6CNiKhKDOTNyspiGZ8TYGlf+TGW87mx2QQRlQMPt+rw93JHWnYekjJykZieY85GuUrHO3kOjqzn+r+RbXBLl7poHhWIA2dTcTAuDU/9vkNd9+P641b7vvbPHnWS2VdPXtkUQ9vUQlRw+XQzJCK6WA59Sjx//rxqM07O1f6cypaxnI9rpIiovOiZp6TMXHPrc1co67uU+VkyZDjQ2wMdY0IxqnNdLHmyn8pWdW8Qhvv6NMD/Xd8abeoEmW+TnVeA1+fuweD3l+NIQrrddVV5+QU4Wsx1REROkZGKjY1VDSd69+5drgdEVNnYtY+IKiqQOpWUiaSMHEPr88ur4qN+uB++HdulSNnj3d9uNF+WrFRqdh6ueHcpvD2qY+LgZri7V33EJmeqNWYv/bVblQfe1KkO3r6hbSU8CyK6XJUqkDpw4IAKovr06YMpU6aU/1GRQ/hRv2wxI0VEFSHYV8tISZMJc+tzF+jYV956NgpHiK+HytT99kB3BPt6YuhHK5CVW6BOr/69G9tPJmHR7rNWQ4J/23RStWWPDLy8glEicvLSPslCderUyW77c3ICLlJP74qBFEsoiahCAim99XkVLu1zpATwn0d645+He6sSwIYR/vj6zs7oZGir/ufW01ZBlJCOgE/9vh3JGbmq3E8yfURElZ6RSk9PR+3atVGdC+/pMuBm1VqeiKh8BPlo3eik2cT5DK31eagfAykhjSWMzSV6NAxHj3HheO3v3fhy5RG1rU6IDwY0qwEfT3eE+nngjbl7sXRfPAa+v0wLpDJzMXFwUzzYr1ElPhMiwuUeSMmMqKFDh6oW56+++mr5HxU5hB/2y5aboeU5k31EVN4ZKQmkkjNzrLaRfROvaqrarDeJDEC4v5dqFy+kZboEVJ8tO4ST5y1Djt+Zvw/9m9VAs5qBlXjURHRZB1LdunXD8uXLMXjwYPj7++Opp54q/yMjcoKMFBFReZHudULmSEkwJVxlhlRl8XJ3Q7+mNeyWZMtg316NwlWHPxlwLKSR35crjqg1V2nZ+aozoDS4ICKq0K59LVu2xMqVK1UwxUDKufBzf/mtkarGfB8RlRNfTzf1U4bOyjopwUDq0kiQ9Nu47mrAsZwe+GGTakKh+2vbaYzr1xCDW9ZEoxr+lXqsROT6HFr0VK9ePRVMEVVlbHlORBUZSKXn5Kn1PIKlfZeuTZ1g1fmvX9MI1C5cZ1U31Be1grzVAGQp9xvy4XJsOnZOzZ7afToFGTl5lX3YRFSVM1K6iIiI8jkSImdsNsGYiojKia+n9idYslE5eVopWoAXA6my7P63cEIfxCZnoV6YH7Jy8/Hmv3swY91x5OabMHLqGvO+Emh9N7YLNh8/r+ZYSaOLeTvP4JYudRHqpzUFISK65ECKnA8/65ctY8tzvrZEVF58vbSMlL4+Snh5sDtuWQer0j5d+Hm547XhrTHxymYY+vEKq6YUx89loN+7S4vcft2RcyrAKk5cShYOxqehc71QeLjx347ocuPw//WJiYkYP348WrRogfDwcISGhlqdiKqSakxJEVE58fXQAynLvCN+GC9/Qb4emP9YHwxvF6Uut64dVOy+y/fHY8neOJxPz8F9321UJwmeTiVlYvXBBAz5cAVu/WIdrv5whdpXBisfTUhHQlo2lu6LQ3ae9awrIrrMM1K33347Dh48iLvvvhuRkZH8oOkE+G9AROR6JEMi9BlSsj6TazQr7rV/f1Q7PNS/MeqF+aL9KwuRmm1/ndT4mZvRolYgNh47ry4v2H22yD4H4tJwx9fri2xvVjMAT1zZFAOb1yjxb7Ws09p+MgkjO9ZhME1UlQOpFStWqIYTbdu2LZ8jIiIiugz4FDab0HnyA3SFksBG79z3wrUt8MmSg3ju6ub4dMlB7DmTihn3dMUTv2xTZX96EGUrzM8Tjw1sjP/2xmHJvvgi1+89k4p7v9uIl65tgTB/L7z81y6VAbupUzT2n03D0Da1VPv70V+sQ2ZuPnbHpuDlYS35BSlRVQ2kmjVrhsxMS10xVT7+uiUict2ufTp9uCxVvBs7RauTkAG+ModK1ld9O7YLBr63TA38vb59bWw/lYyDcWkY0qomnr26uWpoIc0pbu9eTzUM+WLFYZVZktEZt3WLwS8bT2DOttN4fe4e1eBCSMClB13vL9pvdRzfrTmmArfPb++E//t3L35cfxwtowJxc5e6uKFjnUp4ZYioTAOpKVOm4Omnn8YLL7yAVq1awcPDusNQYCCnh1c0fnFVflhmQ0TlxTYDxUDKObi7VVcnfS7VJ7e0x9aTSXi4f2MciU/HvztjMbZXfYT7exX59xt/RSOrbT0bheF8Rg5WHEgwbwvwci9SRihdA6/vUBuf/HcQS/fFo8nz/5qvk2yYnNrUCVLt3KX0j+8VIhcNpIKDg5GSkoL+/ftbbZdZDJKKzs/nwkqqOrz4x4qIyonth2GW9jmnIa1rqZNoXSdInUpLPhc9PqiJOZAa2aEOHu7fCNtOJqFV7SAM/WgFsvMK8H8jW6NHw3C0jApSpYC6O3vUw8qDCSoL9sKfO7H9ZLLKjo3pUQ+TBjdVAd+8nbGYtfmUaqP//NAWCPB2V5mwgS0i1doueV8Zu9ESUSUGUqNHj1ZZqJkzZ7LZBFV5Xu7WpTdERGXFtqkAswxVU4e6IRjWNgobjp7DvX3qo164nzqJuY/0RkZOvgqqxKAWkXh/VFvsPJWi1lE1rRmg5lk98MMmrD18znyfny8/jMY1/NG3SQQemrkFeQVa2eBjP29RgZm0dp+y9JDa1jQyAD/c01WVIBJRJQdSO3fuxJYtW9C0adMyPhQi58OZLkRUYYEUM1JV1ke3tLe7vUHhjCujEe3rYIRh935NI1SQLWuwpNy8U0yImm/11ry96iRBlL+XO9Ky83AoPr3I/e07m4ohHy5X67laRQXhrRvaqC6BXeqHWpWv5+YXqMfQu0kS0YU5/H9Lp06dcOLECQZSTkQWtVL5kJp0IqLyoLc7l1ItwYwU2SMBkJTxTV91VHX0k+G/vd/+DwlplvljTw1phrPJWarzoL7mSppW6PR9JVM1b9cZdX5AsxqITc5S66w/vbUDxkxfj2OJGeq2469oiOHta7Mqg6isA6mHH34Yjz76KCZOnIjWrVsXaTbRpk0bR++SLhXjqDI377HeOBSXrmrWiYjKi2ShMgu0tcUebvxlTvbd07uBOummjO6IJ3/dhjMpWapJxahO0Wr476H4NLUe6q2RbfDjuuNoVisAHWNC8O+OM/j4vwM4mmgJrhbvjTOf7/fuUvN5CcCe+n0HXvlrtwqmpEV7iK+nyla1jQ6uwGdN5PyqmaRLhAOqVy/6jZmsk3LlZhPSPCMoKAjJycku1XVQJqzLYMBpt3XEVa1qVvbhEBGRg9q8NB8pWVoHt24NQvHTfd0r+5CoiopLzUKX1xeXGNTLkOIDcamYsuSQagFvS9rB92msfcHINfJUlZU2NnA4I3XkyJFLPTYqI+/c0BZPpGSpxahEROR6jOV8niyjonJUI8Abk69pgRUH4jFhUBOM+2Ez6oT44OYu0dh3Jk11CKwZ5A2gFtrXDcG0pYeQbzJh49FzKKw+xb3fboSfl5sqN3zx2pb8Epcuew5npKoiV81IERGRa+vx5mKcTs5S5wc2j8SXYzpV9iERWcnMyVflfoM/WF7kuleva6mGERNVNeWWkXrzzTdV2/OxY8dabf/6668RHx+Pp5566uKOmIiI6DLjYZWRYqkUOR8fTzdV+dK2ThC2nUxGg3A/NI70x/xdZ/H92mO4tWsM7v9+E9YdTlQzrCICvREZ4IVJVzVDoxpFuxISVSUOtwj67LPP0KxZsyLbW7ZsiWnTppXVcREREV1WLdDZ/pyc2Ts3tlXDhH+6vxueGdJcbZNM1dYT57Foz1mkZuep7Oq2E0lq/fZtX65DbHJmZR82Ubly+Lf2mTNnUKuWNuHbKCIiArGxsWV1XERERJdXIMX25+TEmkQG4Ikrm6q1VrVDfCAjqLJyC8yDghtE+GH2+J6qlbpkraSj4Dvz9qGgwISvVx5RM69WHkhQ866IqgqHS/uio6OxatUq1K9f32q7bIuKiirLYyMiIqrSjMGT7YBeImcl79V6YX44nJCusk+iS71QtIsOVicJtIZ/ugp/b49Fi6hAvPbPHrXP1KWHVPmfDChuVycYp5Iy0aq2ZV6jLNuPS81GjQAvdgWkqhlI3XvvvXjssceQm5uL/v37q22LFy/GpEmT8MQTT5THMRIREVVJnobZUcxIkSvpEBOiAikp5RO1g33M10kw1bxWIPbEppiDKF9PN2Tl5iM1Kw93Td9g3veDUe3UvKqzKVl4aOZmbDh6Xs3G+t+NbVUwJRkt6Yomw6uJXD6QkkG8iYmJePDBB5GTo03K9vb2Vk0mnnnmmfI4RiIioiqJpX3kqno3Dsdvm06aL9cJtQRSYljbKBVI6WY92AMB3h64adoalYnSvT1vLz5dchAH4tIs+24+hW71w/DZ8kM4FJ+ugrDpd3ZG1wZh6vr8AhMKTCZmccn1Ain5duCtt97C5MmTsWfPHvj4+KBx48bw8vIqnyMkIiKqoowfBL34oZBcSL+mNeBevRryCodM1Q72tbr+mja11LooXeMaASqrtGLSFbj6oxXYeyZVbdfb/wvJYoX4emD1oURM+n27eXtGTr66r3dvbItHf9qKHaeS1RotKRv093JX667C/b3UCIGbO0fDnf8vkbMFUnXr1sWwYcPUSUr6/P390blz5/I9OiIiosskkOK36+RKgnw80KxWAHae0rJOsi7KKDrUF/XD/XAkIV01n9BL86pXr4av7+yM7SeTMWPdMaw4kKC2P3VVM9zfpwEOJ6Rh4HuWmVVv39AGk37bjs3Hk3DDtDU4l65VQ0n8pj+2bum+eFUiOK5fQySk5qiBw/J4ROWl1L+1v//+e5V1Gj9+PMLDwzFq1CjMmDEDSUlabSwRERE5xoNrpMiF9WkcoX6G+3uiVqB3keufu7o52kYHq9bpRlHBPriqVU3c1i1GXW5dOwj39Wmggp5GNbSZVaJemC9u6hRtvqwHUf2aRqimFfrYAOkYKBkw8fF/B9H1jcXo884SDJ+yCl8VdgxcfUgL2IjKUjWTtEhx0K5duzBnzhz8+eef2Lp1K3r06GHOVjVo0ABVdXoxERFRWXrkxy2Ys+20Ov/itS1wV0/rjrhEziwjJw+fLTuMHg3DzOuXHHXyfIZqqW78ImHHyWR8seIw7upZD+3rhuCjxQfw3sL96roZ93RFz0bhqsOffIKV5n56U4reby+xWn9l6/mhzdX/YxJUSalhzaCiwR+RI7HBRQVSRjI76q+//lIn6d4ngZSsoRo6dChcBQMpIiKqDE/8sg2/b9YW7L8+ohVGd9W+oSciC1kD9b8F+9C1fiiualV0lqlu2rJD+L9/96qg7PPbO2LujlikZOYhN78Ai/fGqX1C/TxVZivQ212VGEq7dWmcIY0wiCo8kDJKT0/HggULEBAQgIEDB8JVMJAiIqLK8PTv2/HThhPmtSBSxkREF0c+0s7fdRbRoT5oGWU9n0oyWlL2Z0/neiH4+b7uXE9FDscGDnft27x5Mzw8PNC6dWt1Wcr7pk+fjhYtWuCll17CiBEjHL1LIiKiy5JV1z6ukSK6JFLiJ2uv7G1/4sqmGNyypmp+USvIGzd/vtbccVBmV/21/TRaRgWiYYS/eRiwBGD/7Y1TjTVkXZdkuHLzTRjUogbqh/tzthU5Hkjdf//9ePrpp1UgdfjwYdx8880qePr111+RkZGBDz74oHyOlIiIqIpxNzabYNc+onLVqnaQOolvx3bBrtPJiE/Nxhcrjqi26mJwy0h8dEt7eLm74csVR/D6XG2gsJE0rwjz88SYHvXwcP9G5sArOy9f/X+sX6aqz+FAav/+/WjXrp06L8FTnz59MHPmTKxatUoFVQykiIiISscYPLFrH1HFkYYVckrNylVzqdYePqe2S2ngxF+3q0YXX648bHWbFrUCkZyZqxpaJKbnqHLBRXvOIievQM20+md7rOoo+P6odvh98yk0ivBH94ZhOByfhvVHzqnZW2xwcZkHUpLmLCgoUOcXLVqEa665Rp2Pjo5GQgJbSxIREZUW50gRVS5pMvHjvd1UYLTx6Dk88MNm1UlT76bp5+mGuY/2Vk0pOsWEqGyTBF8P/LAJqw4mqnlYQh8wLIFYixfmq/NS+ff2DW3xwp871VBh+bJk2m0d0L9ZZCU+Y6rUQKpTp0547bXXVDOJZcuWYerUqWr7kSNHEBnJNwYREdFFlfYxI0VUKSQ4Cvf3Uh0BP76lver+t+u0Nuz3imY1EBPmp07G4Ov9m9qh11tLkJOvJRdqB/sUab0uS7Ce/HWb+bJkrsZ+s1HNwKoZ6K3uWxrMTF16CDtPJcPPy02VFMrvBdnn1PlM1A3zU3O0ZB5X2zrBKjiTdVo+nm4V9vpQGQZSUro3evRozJ49G8899xwaNWqktv/2229qnhQRERGVjjELxUCKqPJd2zZKnaTd+sqDCZgwqInd/WoEeuPT0R2w6mACHhvYGMG+njhwNhXXfrJSBVAjO9TBj+uPm//fXjaxH26YukYFW6lZeUjNSsOBuDR8vty6fNDWtpPJ+KswOyYNafTA7Y5uMXj5ulZl/vzJMaVufy6NJUoatpuVlQU3NzfV0c/VsP05ERFVhi+WHzYvZv/74V7mhfBE5JqOJqSrIcHBPp5o9+oCNTRYgqr/3dRWBVrfrjmKdYfPqSBKFxPmixs61MEfW07B39sdfRpHINDHHXVCfFWXwa0nktQaK1mfZfT4wCZoUydIZbaM5u08g392xOLRAY3RqIZ/hT33qqTM25+3adMG9erVw7BhwzB8+HB06dLF6npvby6eIyIicgRL+4iqlnrhlhLAV65rhSV74/DElVpWq3FkAF4b3lr1GziWmIHDCWnYcTIFt3ePUYOCHx7QuMShxLKGS9qzP/vHDqw4kID3F+1X13WpF4oDcakI9PFAj4Zh+HG9Nptu4e4z6NYgTDW1aRDhr0oLJcgb070e6ob5lvtrcTkodUZKMk4LFy5Uc6P+/vtvVU8qjSYksBo0aJBLB1LMSBERUWX4Ye0xPD97pzq/9Ml+Vh/CiIjs+X7NUUz+c9dF397DrRp6NAxHjQAvPDKgMaJDGVRdbGxQ6kDKSG6yZs0azJkzR52OHz+umk9IUHXttdciIiICroSBFBERVYafNxzHU7/vUOdXP91fDf0kIipJXEoWBry3TK210o3uWhcto4Lw/Owdao3WOze0QZi/J04nZSEhLVuVBp5JyYLkwA/Fp5tvF+LrodZatakdxC9yKiqQsnXgwAEVUEm2at26dXjvvfcwfvx4uAoGUkREVBl+Wn8cT8/SAqltL16JIB/XW2dMRBUvLjUL59NzcftX61TgtODxPqo88GBcGk6ez0DfJhHFDgZefShBlQbO33kGhxMsQdXEwU2RX2BS108c3AwdY0JwuUqpyEDKKDExEefOnUPjxsXXeTobBlJERFQZpKvXM4WB1MHXh8Cds6SIyAHp2XkoMJlUS/aLue3Lf+1SQVVscpbVdZGBXlg+6QrVjl2kZOWqwEuaV7SvG4Lc/AK4V69WbLDm6sq82YROMk/2yAsp66QkgHKlIIqIiKiy+BpmwTCIIiJH+Xm5X9JtZWCwePXv3fhq5RHzdWdTslX3v+va1VaXn/tjp2rDLo0rBrWIxILCRhZfjemsGuVk5OTh140nkZmbj3t61b9sfp85/OpLxz4JmmwTWfo2+dmrVy81Zyok5PJNCRIREV2ItC2Wcr6WUayGIKLK8/zQ5uhcLwS+nu7YfPw8Plh0AI/+tBXeHm7oWj9UZaOEzLGS1upixYEEzFh3DGH+Xnjxz504n6G1Z0/KyMWwtlFoXiugymasLrq0b/HixWoQ7+uvv25ugb5+/XpMnjwZzz//vEqD3X///ejatSu++uoruAKW9hERUWXJys1Xg3ndqlftDxxE5BpOJ2Wi99tL1HopiYOeGdIMb8zda3dfTzfLkGBbMh/rqaua4erWteBqym2NVKtWrfD555+jR48eVttXrVqF++67D7t27cKiRYswduxY1c3PFTCQIiIiIiLSLN5zFg/8sAm5+bL+yt2qQ6B4f1RbfPzfQRwu7ADYMMIPcx/tjRGfrsbu2BSrfX+4uyt6NQ7HluPn1SBiCaz8C0sSJQxJzc5D4EWs8XKG2MDhAsZDhw7ZvUPZdvjwYXVe1kglJCQ4etdERERERFTJBjSPxJNXNlXn9SCqXXSw+fqeDcPx2EBt0LAY3TVGNaaY9WAP/PNIL4zsUMd83T87TuNIQjpGfb4Wk37bjus+WYn41GzcOX09mjz/L9q8tABvzbOf8XJ2DgdSHTt2xMSJExEfH2/eJucnTZqEzp07m9uhR0dHl+2REhERERFRhbi5S100MMyWenRgYxUgPTGoCWoEemNo61pqftU1bWrhli511T6ypkrmWb19QxtVEih+XH8C437YhJw8rQRQ5lhd8/EKLN0XrzJeYurSQ6ptu6txuLRv7969quHEkSNHzMHSiRMn0KBBAzVHqkmTJqrRRGpqKm6//Xa4Apb2ERERERFZKygwYe3hRNWNT7JUjkjOyEX7VxeoOVdCyvnu6V1fNbLQ3dixDradTML+s2n4YFQ7DG+vdQmssu3PmzVrht27d2PBggXYv3+/2ta0aVMMGjQI1atrCS4JtIiIiIiIyHVVr14NPRqFX9Rtg3w98MiAxvht00n0ahSOu3vVR2SQt1UgNa5fQ3y27LAKpKT8z9U4FEjl5ubCx8cHW7duxVVXXaVOREREREREtmQdlXEtlejTJALL98ergb4NIvxRI9BLbT+XnoMqvUbKw8MDdevWRX5+fpk8+PLly3HttdciKipK9ZmXkkAjqTp84YUXUKtWLRXADRw4UK2/Mjp37hxGjx6t0m7BwcG4++67kZaWVibHR0REREREZWfCoCbo3iAM392tjVEK9vVUP89nVPFASsgMqWeffVYFMJcqPT0dbdu2xaeffmr3+rfffhsfffQRpk2bhnXr1sHPzw+DBw9GVlaWeR8JoqTl+sKFC/H333+r4EzasBMRERERkXNpFx2MH+/rhh4NtZLBEF8P8yDfKt9son379jh48KAq84uJiVHBjdHmzZsv7kCqVcMff/xhXl8lhyWZqieeeAJPPvmk2iYLviIjI/HNN9/g5ptvxp49e9CiRQts2LABnTp1UvvMmzcPV199NU6ePKluXxpsNkFEREREVPGW7I3DXd9sQMuoQPzzSG9U6WYTFdVIQroCnjlzRpXz6eQJde3aFWvWrFGBlPyUcj49iBKyvzS9kAzWiBEj7N53dna2OhlfLCIiIiIiqljBLpyRcjiQevHFF1ERJIgSkoEyksv6dfKzRo0aVte7u7sjNDTUvI89b775Jl5++eVyOW4iIiIiIiqdQB8tkErJzK36a6REUlISvvzySzzzzDPmtVJS0nfq1Cm4AjluSdXpJ5mDRUREREREFSvQWwuk0nLy1NyqKp2R2r59uyqfkzK7o0eP4t5771UZoFmzZuH48eP47rvvyuTAatasqX6ePXtWde3TyeV27dqZ94mLi7O6XV5engru9Nvb4+XlpU5ERERERFR5Ary1cES6NkgwpQdWVTIjNWHCBNx5552qDbm3t7d5uzR4kI55ZaV+/foqGFq8eLHVWiZZ+9S9e3d1WX5KdmzTpk3mff777z8UFBSotVREREREROS8vD3c4OlW3SXL+xzOSEmHvM8++6zI9tq1a5e4LskemfckHQCNDSZk2K9kuGRe1WOPPYbXXnsNjRs3VoHV5MmTVSc+veFF8+bN1VBgyYpJi3TpJPjQQw+pRhSl7dhHRERERESVm5VKTM9BalYeqnQgJSVx9rrc7d+/HxEREQ7d18aNG3HFFVdYZbvEmDFjVIvzSZMmqVlTMhdKMk+9evVS7c2NmbAZM2ao4GnAgAGqW9/IkSPV7CkiIiIiInJ+AS4aSDk8R+qee+5BYmIifvnlF5U5kjVTbm5uKkvUp08ffPDBB3A1nCNFRERERFQ5hn60ArtOp2D6XZ1xRVPrjtzOHBs4vEbqf//7nyrJk7bjmZmZ6Nu3Lxo1aoSAgAC8/vrrl3rcRERERER0GfF010KSnLwCVOnSPonOFi5ciFWrVmHbtm0qqOrQoYPV4FwiIiIiIqLS0JtNVPlAStezZ091IiIiIiIiutSMVG6+awVSpSrtk+YNWVlZpb5T6aCXmpp6KcdFRERERESXAU8XzUiVKpB6/PHHHQqMpNtefHz8pRwXERERERFdTmuk8qtgaZ809pP24u7upasElCYUREREREREl3WziRdffNGhO73uuutUa3QiIiIiIqLSlPZlM5AiIiIiIiIqnSGta6J+hB+61g/DZdG1j4iIiIiI6FL1bxapTq7G4YG8RERERERElzsGUkRERERERA5iIEVERERERFTegVRJg3ljY2MdvTsiIiIiIqKqH0h16NABW7duLbL9999/R5s2bcrquIiIiIiIiKpOINWvXz9069YNb731lrqcnp6OO++8E7fffjueffbZ8jhGIiIiIiIi125/PmXKFAwdOhT33HMP/v77b1XO5+/vj/Xr16NVq1blc5RERERERESuPkdqyJAhuP766zF16lS4u7vjr7/+YhBFRERERESXDYdL+w4dOoTu3burbNT8+fMxadIkDBs2TP3Mzc0tn6MkIiIiIiJy5UCqXbt2qF+/PrZt24ZBgwbhtddew5IlSzBr1ix06dKlfI6SiIiIiIjIlQMpWSP1008/ITg42LytR48e2LJli+roR0REREREVNVVM5lMJlzmUlJSEBQUhOTkZAQGBlb24RARERERkZPHBg43m/juu++Kva5atWqqDToREREREVFV5nBGKiQkxOqyNJjIyMiAp6cnfH19ce7cObgaZqSIiIiIiMiR2MDhNVLnz5+3OqWlpWHfvn3o1asXfvzxR0fvjoiIiIiIyOU4HEjZ07hxY/zf//0fHn300bK4OyIiIiIioqofSAkZzHv69OmyujsiIiIiIiKn5XCziTlz5lhdliVWsbGx+OSTT9CzZ8+yPDYiIiIiIqKqEUgNHz68SKe+iIgI9O/fH//73//K8tiIiIiIiIiqRiBVUFBQPkdCRERERER0ua2RIiIiIiIiulyUKiM1YcKEUt/he++9dynHQ0REREREVDUCqS1btpTqzmS9FBERERERUVVXqkBqyZIl5X8kREREREREVW2N1OHDh1WrcyIiIiIiostdqQOpxo0bIz4+3nx51KhROHv2bHkdFxERERERkesHUrbZqLlz5yI9Pb08jomIiIiIiMipsf05ERERERFReQVS0pHPtisfu/QREREREdHlqFRd+/TSvjvvvBNeXl7qclZWFh544AH4+flZ7Tdr1qyyP0oiIiIiIiJXDKTGjBljdfm2224rj+MhIiIiIiKqOoHU9OnTy/dIiIiIiIiIXASbTRARERERETmIgRQREREREZGDGEgRERERERE5iIEUERERERGRgxhIEREREREROYiBFBERERERkYMYSBERERERETmIgRQREREREZGDGEgRERERERE5iIEUERERERGRgxhIEREREREROYiBFBERERERkYMYSBERERERETmIgRQREREREZGDGEgRERERERE5iIEUERERERGRgxhIEREREREROYiBFBERERERkYMYSBERERERETmIgRQREREREZGDGEgRERERERFVpUDqpZdeQrVq1axOzZo1M1+flZWF8ePHIywsDP7+/hg5ciTOnj1bqcdMRERERERVn1MHUqJly5aIjY01n1auXGm+7vHHH8dff/2FX3/9FcuWLcPp06dx/fXXV+rxEhERERFR1ecOJ+fu7o6aNWsW2Z6cnIyvvvoKM2fORP/+/dW26dOno3nz5li7di26desGl3VgEZCfDTQbCqeRcQ74436g5fVAu1sq+2iIiIiIiCqV02ekDhw4gKioKDRo0ACjR4/G8ePH1fZNmzYhNzcXAwcONO8rZX9169bFmjVrSrzP7OxspKSkWJ2cRk46MGMk8NOtwJkdlXccJpP15V1/AAcWALMfADZ9U1lHRURERETkFJw6kOratSu++eYbzJs3D1OnTsWRI0fQu3dvpKam4syZM/D09ERwcLDVbSIjI9V1JXnzzTcRFBRkPkVHR8NpnD9qOX94WdkFQqIgH8hKAQoKtOslaPv7cWDfPCAtzrLf8bXA6zWBuZOADV8C5w4D8fss1/89QctQERERERFdppy6tG/IkCHm823atFGBVUxMDH755Rf4+Phc9P0+88wzmDBhgvmyZKScJpg6d8Ry/sgyoMdDpb9t0nFg5ywgpgcw4wagy/1A/+cs188cBRxcWPR2G78GqnsAd/wJ1OupZZ/ysoD1n9l/HFM+sPcfoMPtwObvgf9eBUb9AER3Ac7uAtZMAbKTgXajgUYDATcPy23XfQac2Q4MeQfw9C39cyMiIiIiciJOHUjZkuxTkyZNcPDgQQwaNAg5OTlISkqyykpJ1z57a6qMvLy81MkpnTcEUlJKd2IDEN25dLdd9BKw83fL5eVvAz0fAb4ZCvhFAAcXFX/bglxg/edaIGXMitmq31cL8OY8BNTvDcx5WCIr4KtBwPPxwPfXA2mFGcE9fwGd7wGG/k+7LBmwfydp5w/+BwTVBpoM1tZd+YYB3kFapkyetwRlvqGle95ERERERBXMqUv7bKWlpeHQoUOoVasWOnbsCA8PDyxevNh8/b59+9Qaqu7du8NlGTNS4quBwLrPtfNSkifBSVp80dtJAGIMonRv1gFit5UcROn2z9dK+PbPs399+9uAq960XF7xHuBhyCotf8cSROmkNFCc3AhM7WnZnnoaOLkB+O814OMOwFsxwOwHgb8eAX4cpWXU8rK147FXppiVrJUerv/C/vVEREREROWomsnkvJ9Cn3zySVx77bWqnE9am7/44ovYunUrdu/ejYiICIwbNw5z585V66gCAwPx8MOSHQFWr17t0ONIaZ+slZJOgHI/ler7EcCh/wD/SCDNMBPrpWRgxf+Axa8Aza4Bbp5hfbvUM8D/mpb+ca5+F5j7pGPH9txZwMMbOLYGmH4VUK06YCqwXO/pD+SkoczU6w0cXaGdl2xV7Y5A/8lA7Q7Av08B66Zp1434HIhqD2z+Vlu71eVebR9b8hqlnNLuh4iIiIjoEmIDpy7tO3nyJG655RYkJiaqwKlXr16qtbmcF++//z6qV6+uBvFKJ77BgwdjypQpcGl6pz5ZW7TVECzl5wEbvtbO7/3bzu12OvY4Ec2A3k8CK94t3f7j12tBlIjpDkR3BU6ss95HD6JqtAC6PaiV/xkFRQN9JgIN+mlNLiSgyc/Rslb2AjA9iNIzUBJgpsRqQaQeRIn5zwIZCZbLu2YBY+cDQXWAb67RHkPWcM24EUg5CYz5C6jfp3TPm4iIiIjI1TJSFcVpMlJ5OcBrWpCI678EZt1jue7RbVq2SjroiRfOAdXdLNcvfAFY9SHQ5mat/C833XLdbb8DP4y0fqxJR7Q1SBKASZarIA84ZCmTVFkvPWB7ZAsQ2sD69qs+AhZOLj7bJVkh/Zh0D64DajSzf5uXgi7wTrXJfjnK3VtroKG75SctUA1tqGW53Jz6OwUiIiIicrLYwKXWSFV5mectQUNo/aKZKj2I0svUjKSFuWhyJfDwRiCskeW6Op2ByQnAc2e0znz3/mdp5FCzFTD6F6DvJCCorpYJm7AX6P2EdSbJlnFYcENtILKZXlZnzPo8eaD4IEr0e1b72fhKoNt4y/bIVlqgI4FjzdbWtwmOsb488iugwRX2798YRIkfCwPOVR9oJYFERERERA5gIOVMMgtnM3kHA1EdgLa3WK77+TbrfZNPWpf9JezXztftAQRGaZkWnVeg1oLcw0crq7O3RqhuN+DxHVr2KrCWtuao3W3AFc9bty/XhTUE7vxHa7EuXfmklE/IT/3+Gw4Arv0IuP0PwL9Gyc9dArdRM4Abpmv3rRvwItDnSaBaNe14dCH1gXsMGbTmw4DWNwCDX7e+31ptLcdWnMNLUWrS/OL0lqLzuU5t0v4d5j+nZdem9dYaYWz4Ckg8BJxYX/rHICIiIiKnx3omZyINFWQNkWSkqlcHRkzTMiky18lW8gkZWaydz0jUWpCjmtbmXG9nrpMgxFFym+GflrxPvV7aSQx+QzsZH0vOdxxTuseT0rrm1xTNNIU3tpzvNFablRXR1HK/103RuhVe+ap2ObIlMPEQ8E5hMCbZrdY3amu6vAKARgO0uVkHFwMxPYH5zwB75mgdCHtbZovZlZkEfDEAyEnV2rV3GwfU7wfEbtUad0jweu6Qtq/MyrJt5nHXv9qMr9KS7owbvwI6jNGCWyIiIiJyGgyknIlkkvo/b70twOYDtE+olrmS7nO69MJ26PLhXl/rI6V90pyholxMsFYcCc5kXpV3IBBSz7Ld3RO46g3rfduP1k5GfuFaJkuCrpYjtKC05XDrgExOcXst2xa/rGXsJDgqjgwbliBKD16ldTvkVEgPooqz7Setq2D8Xm0Nmm2po2QZfcO19vNZScDuOdq6NcmA3fqzdZt7aRYiZZlEREREVCkYSDk7Y0MJfW3Slu+1D+S69Djtp7F8ru9TQH4u0P52uBzpDjhmzqXdx4WyS0IyW1KauP4zS/e/JlcVXZ+mS40t3WO3GQVsLwx8jGQtlr4ea+N04LEdWpAnjqwAvh8OhDcF4nZZ304CKykflPfC5u+0WVsSND+xT1srN3ciUKejls0kIiIiogrBNVLOruNd2popKXcbt9pS9iZZDZ0+oFcv61Pnw4FrP9A+YFPxWbSr3wZeOK+tpZKugLtnF7+/ngWUYKck0rDDqOdjdu7rJDClK/D7vcCO34Bvr9E6J9oGUbrf7wYSDgD/PGHJiP1+DzC1B7D/Xy07dnorKoRkxSTbef5oxTweERERkRNiIOXspPGCdLyT9uey/sc3xJKlOLTEurTPGEhR6UlWSNYhCenkZysvGziwSAtkRNMhwEObAP+a9u9PyhJ10uhCygzdvKy7KAppELLjFy1IKk6dLtpPWSf3SSfrtW8S9GWnWC7bO/bSSj5lmUUm3SNl4LEEeTnpQLphRpdYO1Vrxf/VlUBuJnB0FbDpGy1rZktu+911wF+PAgWX0L6eiIiIyMkwkHIFsjZIX4MkJV26fyYUX9pHjpGSyWpuWve9vXOtr5N5WDNGaiWVIrA2EN4IGFV4WXR/SAt4H98NBEQC130KxPTShh5LoCaNLnQSWNnT+V7ry1J2KJ0R9dbwxnVy9shw5fdaAAtf1LJGMrz4o/bA7PHaZXuk/HPOI8D7LYBpvbTnP+MmbeCxBHlvRGmNO7YZShXXTdV+pp0F3m8JfHO1FihJEw/bx1n2ltYVUQKto8uBlNNA6ln7x1Lk2PKKP24iIiKiSsZAytUYm094+GofNPWht8xIXbyAmkCX+7TztuubbJt26A0wgutatkm7eAlkg2prl9vfBtz1D+BXGPjWaqP99PQH6ve2ntMlarSwbnQhTUek7FCC6H5PAY0GWa7rbpizJWWG0jLeWH4os7FeDgbea6bNHtv6g5ZN+3oI8M01WoAiZXlfXwV81tcwR8sErJ0GnLTTql32kffagslaEw+d6hhZSLoUSpt+afs+82bg407A+s8t10uTjE+7AR930NrEH1+HYknLeAngvhpkP9NFREREVMnYbMLVSKlXZGvg7A4gOxU4vsZynXS5o0vLSkm2xTjzKTsNSDxoXbanDxr2j9RmZsm/gwwSLonM05JyuS6FWScZMtzhDi3wkGHLA18GQhto87HOH9FasxtJkHdwofaYPR7WOvxJ8NW1MPjbNB04srz4x1/5PnB8tXb+lzuAA/O1NVm2JAslwhoD4U2Aff9Y2u0fWw2s/qjobep2t7wP9/6tneyRRhm6NZ9oWarHdwE+wVqw9NtYICdNm0sm2T/pXHhyA/BuE60JiLzuMiJAAlEpt5SALqqd5T4l0NNHBUi3Rj2Le3KTdl4fFE1ERERUBqqZTKydSUlJQVBQEJKTkxEY6ALBiJRsSbZBStGu/RCY85C2XUrLWN538WQ90Jt1tKYT0hFPslSy/kdK1wKigCf2FL2N/O8jQYDedv5SSRe+tDhLBsv4OBKgSOAmbfLtHYesV5JskqxJsiXZSn0tXWk0vRq45UdtjZOayVVNC+b0DocS0Mft1h5z/Hptzd6C5+zf1+jfgBk32L9OAki5X1nf9UdhUHgh9XprZYX6EGrJEPZ6XFvjteELbZsMkpbujyv+p6350rOGN/+ozeSK26OVaOpfPuRkABu+1P7NZe6YBF7ympZlW38iIiKqUrEBM1KuSLIS7t7asN7Tm7VtrW9iEHWpPP20Urn4PdrsJmkqcbawAYMx82EkH7TLKogS8kFeTvYep/m1xd9Orvf0BRr0A15MsgQAkvWR9UsXCqJGfG4dyOilh7ImzydEC0b0IErKS4dP1QJOaXYh68XCH9LmbG2baX2/49YAkS20Nvz6GrPGgwFTPnBwEbDoRe3kiKMrrC9LmaI8R6MlhvleOvk3nXEj0OMh4I/7ted1z2Ktoct/rwJrp2j7yTwxyXj9OwnoOg5od4sWpEnWUda7SdOMw0u0jF1wtP1jlHVgbh6AbzHr2YiIiMjlcY2UK5IPc/p6KH2oLIOosiFZC6G3Epc1RvqAY1dhzKKESjbJDuk4KLPGdMYgrccjQJ1OlvsytnuXwOrR7VrwFNHEsp+ILuwwKNqN1hplSBClZ550jQdpmaEgmyBEyvaGvFP65+kVBAx6Fahm+DVW23A8Osl4tSrMiElJrARRQoJDWdf1612WIEpIuaV0UpT1XxKQSUONH0dpmd+8HG2O1w/XAx93BLbOBP58CJg7Sbs/yWItegn4XxPg7QbAS0HA1J7a+jBp7CHi92m3M86CE3I9OxsSERG5DGakXJXMlpJ1Kwn7tMsMpMqGZJ4kq3Jmu3UgJeuXXJHe/MLWkLe0NWHV3YGYHlo2S19712qk9b6SsTmx1rIeShpg2GMMpOS+6/WyXJamGxK4HVio3b/cx43fAl/2t+zT9QFtzZcMSpa5WAMma0GSrIWSbn/SZCN2O/D1lVo2STokynF3GgtkJGgz1iSQmXWPFrhIm/k75mj7CNlvzsPAuUPFNxIpydYZ2kmXnw3MHmd9X21u0tajKYVV05LVlPVftqTxiKx3k8B16RvA6o8tTUxqttEGMEvJoZR55mYBdbsCLeyUbRIREVGl4BopV1wjJaT7mrHEadjHWvMCujT7/gV+vFnLTN23VOs8l3gAuONPrWzO1UiJ2mt2gux7/is6rFnWZ0mWRM8i6dZ9ppW5CWnnLgGOPbJW7IsrtAYd8tqVpvmJBEgfttXKKh/eXLpSOCkJDK6nZcXsyUwCTm0E6vcrWnYpv+5SY4HqHsC7Nrev0VJb96UHQFe+rpVZunlqTS+MjTbkul2ztHbx9kjr+pbDgd1ztAYa8jM7GZes+TCtGcemb7Vjkter39OWTpJCmp+4+5RtySkREdFlJIVrpKo4+Ube9tttunR6Zk8aPkiplgRRrpyRcjcMAjayDZZKWp8lrdyljbl0L2x+TfGPJRmU+5Y51nxDGmeMW63N2SrteqJGA0u+XroAFrePlCrqzTpaXq8FQz0f0zI9kgk7uFgL6hr2ty6RbNBXC14kGLrmA6DTXdoXF5Lhkqxeu1u1cj+dZM9CYrRsn+g9Afiki2WgstzH7j+1tVY6fd2jkOBNsluyHnLrj0DaGW1N2p452slo7z/AqB+0bKFk4+R+pQRTZerOaSWK0hRE1pJJ50Mp49w3VwuyJeMngZi879XcroLiM45ERERkhRkpV81IyRBV8/yfws5osvaELo20FZc1MZKxkMyLDOOVluSPbHHdDm6yTkcngY58SLcXSF1IVetiJx0JZVhwi+GlC/xObdbKDCXosvc6SNe/f57QShivsBmiLGSWV36Ott5OAly5/FkfbW3UmDmWoMveay2XJSuoz+WS4K3jXdrxS7Af3Q3oOMa61NARsqbtxDotWJbGNYNe1gJOCYpn3at1r5QGIdJoRAK+PhO16yUAlgA6qM7FPS4REZELxwYMpFw1kFrwvGVNhbhrHhDTvTKPqGqQZgKvFTbykCG4MrvpiueAvoWlba5oxXtaM4Vbf+EspfIma5r0NVmlIWWIEhR5lSKjLL+q103TMlADXtDWpElg90FrLbPU7BotWyVZp2NrtICnVlut/LBma21NpZQ1evhpjT1k3ZrMMCupzbwEefr6uGIVdpSUrJZkueQ429wIHF6mBVuS0ZROiERERC6CgVRVD6SWvqUtUNc9sFL7sESX7s1ora23TyiQeQ648RttwKsrq2rZJCr+/XrDdC1g9vC9cBMayYpJFkvWW0ngJOWNsp4rPa5o58MazbXATzoO6iWvpSWNTKRphsz7kiyarEGM6qB1IJX3ppTRSrmyvEcTD2lDqeU4pHzS2BmSiIioAnCNVFUn6ziMpMSGyoZ8oJMPpvKhVEg3OFfHIKrqknI74/s1tL5184mShDcG7lmklTnKuip5nwx8Cdg7V8t2ydos6X7Y9mbLbbqNA+L3agGRBGtS+iodC6XJhQyE1gcl64GdkG6QcjJ2PZQMWo0WwPK3tcttb9UCQGlJr9s1Gxi3Ejhb2ARE1vDpIwqEZMBkTZj8bHW9lrFLOqFl7DjDi4iIyhkDKVdlWwrkyUCqzMgHsKRjlstsLU/OTMrn9DEIQgIbR/mFW38p03ZU8ftKmZ7KKBkCmiZXWs7PHq+VxN42Swty9EDJ1t6/tZNOxg7YDnSW5h5SumgkpbZ6I45vhlo6J8qcL51sk7b9Ul4oLealnfy8p7WMW//JWqApjy2ljxJMSlMOKYuU7TnpQEQz7fZEREQlYCDlqmy79JVmjQWVjnwzbyTrSYicOSNl27WwMg3/1HI+sqWWwZJ5bF8O0LaN+UsLdGRwsT3SOOOGr7XGHNJOX8r+jJa8rp1KInPg9Flwiw3DoKUk0Ri8uXlpa82MoySM/9/H9NTmm0kmbO1UbY2ZDHeWUsisZKD/c9rcL2n5L9k3yYzJ7w9mw4iILgsMpKpCICUd5oprc02Ok5IknSziZ2t5cmbGwCmyFZyKlApKUCGnkV9pg8SlGUbtTsCaT7WSwrHzgd/v1pphCCnv0wdJP7oNOH9M6yY4/zkgN0Nr0S6ljLqBL2uZpLkTtbb30hhGgqddf1gfS5CsJUsFspKshyobgyjJ7OekauclUJI28XIy2j/Pcn77T1pmLO2s9T6S0bpF2tbHaeWMkiWUphsSHAbWLvtSW1lnJq3z2dSDiKhCMZByVcYMFLNRZcv4bbJ8K831ReTMJNjXyXonZ9X6Bst56WwoDXIk2yRNLCSLowdSfoVdM/X//6RJhZxkDZSQrM/O37XASbJBne/RfgfKAGSdNIgZ9gng5gH8+RCQcgq48VttOPLxNdpMLWl2seJdLbsk7d9ltpfM0JLrpg8FUk6W7nnZBlFC1pDNvFkrEdZng+mZMWnqMfgNLVsnreNLUlCg3Zf8Dlr5vrat6dXa+jIhLemPrQYWvQic3aWVU9btxi/WiIgqCAOpqtBsguujyi8jxbI+cnbdHwL2z9cCCVfKSBgHQBvLaS+0JlGCBBmA3PpGoJqb1vnPHv0LppFfGDZGaM04dA36AbFbgTpdLPcj2a0Ju6xb2ksWK/mU9tjS0VDWUZ3eCqx8T9snqC5w609a84wtP2jrtfR1a6ENtUyUHihKBuyz3lqWSgJf4/o0XeoZrWnH2mnAvn+sr9v+c/GvzXfDDM8/UMumtxhmeV+0uw3ISADOHdGyejLEWYJUPaulD9OWRiN+NQAP7+Ifi4iIGEi5LGPwxIxU+WakiJxZYC3g4Y1wacZgwpiRKolkmy6VBAqSwSmJZM/kJE09jGQw88AXi+7f4XZg9UeW7oUy3LhuD2D/v1oWatnbWrmgtHiXTNJ1hjVlEsjI9cvf0bJNRjLIufGVwKZvgdx0w/H5azPDJEAykkBJTtLJUKdntWxb00uDEVlTJoGilEBKcKkHuLJmLbyRto88jpRjSgdFyaxJVk9+X0oJowTAUnZ5dqdW2qhnxXIztdsxs09EVRADqSqRkWIgVaYYSBFVLOmmp6sK4wYkyNADKRnsLQGbrJESsnZKBiZPv0rLXrUYDjQepF0nTTRW/M9yP15BWvMOyShJMCYBnTS/kH1krZnM2JJsmGTTZO7W6S1a9kzWlfWeoK0tO7hIux9pV6+XK0Z31bJ5MmxZ2tLrpPQxI9FyWc5LRszQFNJs1YfaMGYJkCSAkkyelCImHwfcfbS/UbLOrCBXC8gkiJQsl5QzFuQB9XppWTl5DPkZ1U4rq5Qh0FKuqrfwl3VsHO9BRE6KgZSrMmahqvOfsUyxtI+oYnW8Czi4WAsYwpvA5fV+QivPU+WWdsrjYroDrW8Cdvyizc16ZKuWjdr8vXb9VW9ZZnfZdmEMiQGGfVT0PqWET05GEqx0vFM7nxKrZbqaXa015RAScMXt0YIvN0+tK2HcbqD5MGD951pzEAneJECSgEa6E0ppZPw+4MR6rTGHlASq+zpqedy8TO2kk2DpyHLrY5N1biWR370SiElQJSWTsh5OWtlL9ksPvOV3depprcxSSial2YoMfdazXxxETkTlrJrJpP8WvHyVdnqxU5E/bK+EaOflm8qx/1b2EVUdsdu1NQxCvkU2lt4QEZWFzCTgnUZaxubBtVpw8usYLTh4cn/ZlC6Wtw1fAnv+0oK1Mzu0eVzNhmpZfQmc5OOFNMeQhhzJJ7WmHxKoSZORuL2Ad6C29sxYqlgWpDOiPJYMV5fyQymvzErRgjv/mtoaMMly1S/8PU9EdJGxAVMZrsq4wJrfuJVfaZ+UxBARlTXJNDXoq5XeHVqiDQQWnca6RhAlpGOinETLEdbX1e5oOV+jWcn3k3RCa8YhgU9YYy37Nes+4MgKLcsm93VgYWEr+lJ89ytBlJCA7dBi7WRPnc5a8Cdt8et21daLSRdHybrJsOZtPwH1+wKd7tLW8ckxSJas0QAtMyYljWd2aoGi/JtJcCx/P6Q8Ue5DsnxSoijrxy5UnijZPlmnJsfkKv/+RMRAiqjE0r7iOoIREV0qWaskgdSBBVpbdllzpAcml5PgaO2kk86BN8+w3keCGQl6pHGFvF5SShjWUOu4KG3mJQA5skwLSGStmJRWNr0KWPkBcGqjNrha1mpJMwydBE06yazJyZaUDC55TSsxlMDpYsgxtxmlDaaWY5OgTLo5yjFK8Cil+rJuLjtZmwsp5ZfhTbWgTZ6TNO6QBh4yM+3YKu0+a7YBIgrLYA8s0rpESjmkBH4idpu2Li09XmtUIgGeBIyyPk0e00gvgZSf0qBEOj7yC1qiUmEgVSXwF16ZkjUBxhJKIqLyIGVn4vAS7ad8WJYujFRytYBxZpgIiLS0sxfNC+dsqfPXagGEnhGSOWTSvl5KDaUBhwxuloyXBBA7Z2nBlwQhUv4nga6sv5IMWXFBlAQv0lxE5n2lnNaGRutBlzT0kO6LMkts87fWTT2Ma8qMpNRz959Ft8sx2ba+14MmCbasPg/YZO3cvLQMqASiEiDJWjIJ4mS9nayLk0yaZNmOr9MakkjAWbO1tp9qie+hZdnkdZBxCxKUSkt/2/V7RJchBlKuTBbgSr25dDui8iFtf4mIyoPt727JIFDZM5bVSXZHThKUyQytno9YrpP1sBI0SMt2KQuUlvcSeEkW6NQmoOX1WkZHAi4pwZT7kfI945dvOsmMSedCCUROrAMWPA+c3qw1w5AsmnRMlKxT4gEtSGlylfbvLwOWt/2olQ5KeWB0Fy3wk7b4EoBJl15ptiHHYxVA6UyW4EkCHyE/jYOj5TiENOqw1/xDmoNIkCknWxu/tgwBl+OVRjGd7wb+eRLYP0+bDScBpby+Da4AutyrtdSX45XXVJU9NtCeg2TApG2/ap1fmBWT11uCXLkfY3diIifFZhOu2mxCSCmALOiVTkb2fpHTxds6Uyu1Gfoe69WJqPz8r5mWFRDybf/g1yv7iKg8SHWDBCj+pZyTZkuCEykDlbb3su5KSgRlbZ28dyTgk5JQKQvc8r0WkLS/XQuE9KHXEhxKtmrvX8DZ3VqjjRMbtH0laycZOAlcpDmIBDyyXksaiEg2TYI6eYzDy7T5Z7Ye3gx8XJhdtSWBnwRVcv/FkZEHksmTj6MSLMpjyjw5GQ2we7b2vCSobHuLFoRt/ErrOKnKFIO1UkW5LNk//0ig3Wit7FOOVzJrsp8EmNKERLK+8pzz87SAkOX7dImxAQMpVw6kiIjItX1/vaUZwqBXrTMkRM5EskkSXElQJWuvpvXStvd7Blj6pnZ+5FdaYCTzxNZM0danGatopHuiagZSSR89ZZaZBFsy500Cx45jtGyZBFitbrCUPupfTudmAQfmayWcEqylxwEBUVomUgI+WX8mIxvObNOe99ldWqZZAkhZg1ejuda6X0oppcRU1ssZ1wOWRDKRcpKMnWA7/wrFrn1ERETOTr5h1wMpPXtA5IykOqN2B8t7td1twNYftO6Goq7MR7tBOx/dGWgyBJg7UZuX1v95oOejlplmUu4owYU02lj9CRDZAuhyvxbcSMbt97u1UkGZaSa3k5LCrYUNSORx63TUyiCFBC0evkBUey0oWjdVm68p2SgJbGTNlzEQkpPIzAZWvq+dl+oemZ0mZH9ZvyjZMdUE5hJ5Bmgz13SSbcvJ0J6zNA2RY5O5bNJcRIIzWa8ngVp6gtayX66XDsJy3NIIRF4zCUilNLXxYO12sm5QZsJlntMamcjrKxnMWu20NYSyZk8CRAnmJJCVoE8CYlnHJyWarGq6aMxIMSNFRESVZfXH2voZccccrSU6kStY8ykw/1nLZQmErn7bfibL0RJ5+WgqZYTygb+6m1aKt2m6lt1pMrjk20rwJN13pXGLrFGTEj59/ZWUREqAJNdLUw8JxqTjo5QyntxoPUjaSNZ5STAigY8EI5JxSz1rWYdWGhIQydo5KUF0JhK0yXw1ISWfUgYq2TSZCddnohZYyuuXFq+VjkpWTf49VefMRkC7W7WgWIJYeY2kEYm8phIUS2MS2Sb/hi6GpX0OYCBFRESVYvcc4JfbtfMPrrvwzCUiZyEfpKf2smRbpKxPz0i5qrwc4PhqIPGQ9vwkuOo7EWjY3/7+sdu17I8EI9IqXzJv0glSSvJ6PwEseR1IOABc+RoQ3lhb6ybZL1m3JWvYJAMl2amY7lqm6twRLbtWs5UW3MjvAzkOCd48fLQB1rIuTNax/TZWu71OSgwlQJRmIhI8drhDC2IkMJKTlGVKtkuCH7mceFDL3EkW60JZ8+HTtM6TelbQyNNfC06LI2vc5PnLa7jvH22NntxnvZ7Alh+0YFeGczcaqAVvToKBlAMYSBERUaWQb8g/L2zb/dwZ7cMSkauQIGHe01rJ2YjPAHfPyj6iy4cEexKESOt9Ke9rf5tlHphkhaSrZGkzf9IaXxqCSMdICa6ktf6y/9MCNiFlmtIlWma22a45Sy8slZRMlWSiZP2cHNPFuPZDoOOdF3fbMsZAygEMpIiIqFLIn+BVH2glTK2ur+yjISLSyHwxWSP24832r286FBgxTVtbJoFc36e09VvyO03KKCUDtnaKFoBJ9kuycBJsSdZJ5rOdO6SVarYaqa0RkzEDka2BcSvhDNhsgoiIyNnJN8gyW4iIyJlICaHMNwuqq60JE2GNtdln6nxDrSSvz5NFf6d5eGsNR6KnW7ZLsw9ZN+UXrmW69v6jZbr8wrT1V+82As7u0LJj+vBtF8BAioiIiIiIigZFg18DVn2oZZkk6yTrr/b8pTWicERIjOW8zP6SUkSdzFeTdVPnDmuNPxoUlju7AAZSRERERERUVIvrtJNR82vK/nFCCwOpkoY3OyGOdCYiIiIiosoTVDioOPkkXAkzUkREREREVHk636MNFo5wrREQDKSIiIiIiKjy1GwFV8TSPiIiIiIiIgcxkCIiIiIiInIQAykiIiIiIiIHMZAiIiIiIiJyEAMpIiIiIiIiBzGQIiIiIiIichADKSIiIiIiIgcxkCIiIiIiInIQAykiIiIiIiIHMZAiIiIiIiJyEAMpIiIiIiIiBzGQIiIiIiIichADKSIiIiIiIgcxkCIiIiIiInKQu6M3qIpMJpP6mZKSUtmHQkRERERElUiPCfQYoTgMpACkpqaqn9HR0ZV9KERERERE5CQxQlBQULHXVzNdKNS6DBQUFOD06dMICAhAtWrVKvtwXDJqlyD0xIkTCAwMrOzDoSqG7y8qL3xvUXni+4vKC99b5U/CIwmioqKiUL168SuhmJGShWLVq6NOnTqVfRguT/5n5v/QVF74/qLywvcWlSe+v6i88L1VvkrKROnYbIKIiIiIiMhBDKSIiIiIiIgcxECKLpmXlxdefPFF9ZOorPH9ReWF7y0qT3x/UXnhe8t5sNkEERERERGRg5iRIiIiIiIichADKSIiIiIiIgcxkCIiIiIiInIQAykiIiIiIiIHMZAi5aWXXkK1atWsTs2aNTNfn5WVhfHjxyMsLAz+/v4YOXIkzp49a3Ufx48fx9ChQ+Hr64saNWpg4sSJyMvLs9pn6dKl6NChg+o006hRI3zzzTcV9hyp8pw6dQq33Xabev/4+PigdevW2Lhxo/l66XnzwgsvoFatWur6gQMH4sCBA1b3ce7cOYwePVoNHwwODsbdd9+NtLQ0q322b9+O3r17w9vbW019f/vttyvsOVLlqFevXpHfXXKS31eCv7voYuXn52Py5MmoX7+++r3UsGFDvPrqq+r3lY6/u+hSpKam4rHHHkNMTIx6//To0QMbNmwwX8/3lwuQrn1EL774oqlly5am2NhY8yk+Pt58/QMPPGCKjo42LV682LRx40ZTt27dTD169DBfn5eXZ2rVqpVp4MCBpi1btpjmzp1rCg8PNz3zzDPmfQ4fPmzy9fU1TZgwwbR7927Txx9/bHJzczPNmzevwp8vVZxz586ZYmJiTHfeeadp3bp16n0wf/5808GDB837/N///Z8pKCjINHv2bNO2bdtMw4YNM9WvX9+UmZlp3ueqq64ytW3b1rR27VrTihUrTI0aNTLdcsst5uuTk5NNkZGRptGjR5t27txp+vHHH00+Pj6mzz77rMKfM1WcuLg4q99bCxculE+5piVLlqjr+buLLtbrr79uCgsLM/3999+mI0eOmH799VeTv7+/6cMPPzTvw99ddCluuukmU4sWLUzLli0zHThwQH0WCwwMNJ08eVJdz/eX82MgRYr8zyv/I9qTlJRk8vDwUH9EdHv27FEfVtasWaMuy4eP6tWrm86cOWPeZ+rUqeoXQnZ2tro8adIkFawZjRo1yjR48OByelbkDJ566ilTr169ir2+oKDAVLNmTdM777xj9Z7z8vJSv/CFfHiV99uGDRvM+/z777+matWqmU6dOqUuT5kyxRQSEmJ+v+mP3bRp03J6ZuSMHn30UVPDhg3V+4q/u+hSDB061DR27Firbddff736QCr4u4suRUZGhvpCRgJ1ow4dOpiee+45vr9cBEv7yEzSxVFRUWjQoIFKE0u5i9i0aRNyc3NVSlknZX9169bFmjVr1GX5KeVakZGR5n0GDx6MlJQU7Nq1y7yP8T70ffT7oKppzpw56NSpE2688UZVNtW+fXt88cUX5uuPHDmCM2fOWL03goKC0LVrV6v3l5QsyP3oZP/q1atj3bp15n369OkDT09Pq/fXvn37cP78+Qp6tlSZcnJy8MMPP2Ds2LGqvI+/u+hSSJnV4sWLsX//fnV527ZtWLlyJYYMGaIu83cXXQopH5byUSm3M5ISPnmf8f3lGhhIkSL/Y0rN/7x58zB16lT1P7DU00r9rvyPLP8Dyv+sRvLBQ64T8tP4QUS/Xr+upH3kA0tmZmY5P0OqLIcPH1bvqcaNG2P+/PkYN24cHnnkEXz77bdW7w977w3je0eCMCN3d3eEhoY69B6kqm327NlISkrCnXfeqS7zdxddiqeffho333yzCr49PDzUl0CynkW+aBT83UWXIiAgAN27d1fr7k6fPq2CKvkiSAKf2NhYvr9chHtlHwA5B/0bNtGmTRsVWMnix19++UV9O0J0sQoKCtS3ZW+88Ya6LB9Gdu7ciWnTpmHMmDGVfXhUhXz11Vfqd5lk1okulfz9mzFjBmbOnImWLVti69atKpCS9xd/d1FZ+P7771UGvXbt2nBzc1MNbW655RaVTSfXwIwU2SXf4DZp0gQHDx5EzZo1VcmMfNNrJJ2v5DohP207YemXL7SPdJphsFZ1SbehFi1aWG1r3ry5uXRUf3/Ye28Y3ztxcXFFyiKkW5Ej70Gquo4dO4ZFixbhnnvuMW/j7y66FNK9Uc9KSfnn7bffjscffxxvvvmmup6/u+hSSSfIZcuWqS57J06cwPr161U5siyx4PvLNTCQIrvkf+pDhw6pD8EdO3ZUZQ1SK66T2lr5ICxpaSE/d+zYYfU/9MKFC9UHDf1DtOxjvA99H/0+qGrq2bOner8YyZoDyXgKaS0sv8yN7w0pmZL6buP7Sz4MG7+l+++//1S2S7Kn+j7Lly9Xf4SM76+mTZsiJCSk3J8nVa7p06erEhdpY67j7y66FBkZGWqtiZFkDeT3juDvLiorfn5+6vOWrFmSEvjrrruO7y9XUdndLsg5PPHEE6alS5eqFq+rVq1SrYClBbC0FtZbCNetW9f033//qRbC3bt3VyfbFsJXXnmlaevWraotcEREhN0WwhMnTlSdsz799FO2EL4MrF+/3uTu7q5aCUt71xkzZqj3wQ8//GDeR1q8BgcHm/7880/T9u3bTdddd53dFq/t27dXLdRXrlxpaty4sVWLV+lmJC1eb7/9dtXi9aefflKPwxavVV9+fr76/SSdqGzxdxddrDFjxphq165tbn8+a9Ys9XdRujjq+LuLLoX8DpEue/I7ZsGCBap7cteuXU05OTnqer6/nB8DKTK38q1Vq5bJ09NT/eGQy8Y5P/I/7YMPPqhaaMr/gCNGjFAzW4yOHj1qGjJkiJpPIH9sJDjLzc212kdmu7Rr1049ToMGDUzTp0+vsOdIleevv/5SH1albWuzZs1Mn3/+udX10uZ18uTJ6pe97DNgwADTvn37rPZJTExUfxxkjou0pr7rrrtMqampVvvInA1ptS73Ie9j+SNEVZ/MJZPvBW3fM4K/u+hipaSkqHb6Eoh7e3urf3dpS21sI83fXXQpfv75Z/W+kt8r0up8/PjxKvDR8f3l/KrJfyo7K0ZERERERORKuEaKiIiIiIjIQQykiIiIiIiIHMRAioiIiIiIyEEMpIiIiIiIiBzEQIqIiIiIiMhBDKSIiIiIiIgcxECKiIiIiIjIQQykiIiIiIiIHMRAioiIytVLL72Edu3awVlUq1YNs2fPdug29erVU7eTU1JSUrkdmyvTX5/g4ODKPhQiogrBQIqIqAqYNm0aAgICkJeXZ96WlpYGDw8P9OvXz2rfpUuXqg+8hw4dQlVW1gHcK6+8gtjYWAQFBRW5rlmzZvDy8sKZM2dQ0Y4ePar+Pbdu3YrKJK/NBx98UKnHQERUkRhIERFVAVdccYUKnDZu3GjetmLFCtSsWRPr1q1DVlaWefuSJUtQt25dNGzYsJKO1jVJoCqvpwQtRitXrkRmZiZuuOEGfPvtt3BWOTk55Xr/8trYCzKJiKoqBlJERFVA06ZNUatWLZVt0sn56667DvXr18fatWuttkvgJb7//nt06tTJHCTceuutiIuLU9cVFBSgTp06mDp1qtVjbdmyBdWrV8exY8fUZSl1u+eeexAREYHAwED0798f27ZtK/F4v/zySzRv3hze3t4qmzNlypQiGZZZs2ap4/T19UXbtm2xZs0aq/v44osvEB0dra4fMWIE3nvvPXNZ2TfffIOXX35ZHYdecibbdAkJCeo2ctvGjRtjzpw5uFhfffWVet1uv/12fP3113bLAt944w2MHTtWvc4SxH7++edW+6xevVplz+T1kH8PKT00ZpnOnz+P0aNHq9fYx8dHHfP06dPVdfLvK9q3b69uo2cg77zzTgwfPhyvv/46oqKi1HtE7NixQ/0byf2EhYXhvvvuU0G4Tr+dHHNkZKR6TSUbJ9nOiRMnIjQ0VL0v9McnIrpcMZAiIqoiJOiQbJNOzsuH6r59+5q3S+ZEMlR6IJWbm4tXX31VBRzy4V2CGPkgLSRYuuWWWzBz5kyrx/n/9u42tMY/juP4b5w8maxF82APVohyn0Kj7QlyO0/ElNVakvvmLjcZe0CRSKE0lBKmeCI3czeFPLFQMnOXRQhF2YjFXP/e3/qdrrOdo3M4/v47/8+rrnauc51znd/1O9L17fv9fc+xY8fc+PHjXUFBge3Pnj3bgq+6ujp3+/ZtN2rUKDdhwgT34cOHuOPk/Zs3b7Yb/KamJrth37RpU6dszsaNG92aNWssmBg4cKCNxZcu3rx50y1atMhVVlba8UmTJtn5vNLSUrd69Wo3ZMgQKzlj4zmPIGvOnDnu3r17btq0aRakJBrvz7S2trqTJ0+6srIyG8PHjx8tE9jRrl27LEAiCF2yZIlbvHixe/TokR1raWlxJSUlbtiwYe7OnTv2faxbty7m/czPgwcPbI6ZM4LbPn362LFbt27Z3ytXrth1EoB69fX19jmXL192Z8+edZ8/f3aTJ092ubm5rqGhwcbO+5YtWxbzeVevXnWvX792169ftwC1urrazZgxw97Hvx/mfuHChe7ly5cpz5mISMYIREQkIxw8eDDIzs4Ovn37FrS0tASRSCR49+5dcPz48aC4uNheU19fH/Bf//Pnz+Oeo6GhwY63trba/t27d4OsrKzo69vb24P8/Pxg//79tn/jxo2gV69ewdevX2PO079//6CmpsYeV1dXByNGjIg5xpjCtmzZEhQWFtrj5uZmG8OhQ4eixxsbG+25pqYm2y8tLQ2mT58ec4558+YFOTk50f2On+txnqqqquj+p0+f7Lm6urqEc1tQUBDs3r270/MHDhwIRo4cGd2vrKwMysvLO723rKwsuv/jx48gLy8vOof87d27d/Dly5eY75IxMf8oKSkJKioq4o7Nz5d/rcc4+vbtG7S1tcWMNzc3167ZO3fuXNCtW7fgzZs30fcxZr5rb9CgQUFRUVF0//v37/Zvrba2NuYzDx8+HPMdiIhkMmWkREQyBNknMg5kGsiKkMWhFIyMlF8nRVlfv379rLwMZJDIhrBP2RmvxYsXL+wv5WaU4Pms1LVr1yz7RBYKZLIoC6NErGfPntGtubk5bjMLxsfz8+fPj3n91q1bO71++PDh0ceULcKXHZJlGTNmTMzrO+7/TPjc2dnZVpLoz50KSvnIRnk8JstDpirR51F+Rxll+Fo4Tllfomshg3XixAn7PtauXWulgMkgy9WjR4/oPtksyiS5Zo/sImWcPkMGMnlkJD1K/DiX1717d/vOf2XOREQyReRvD0BERNJjwIABtnaFMj7W1PigiPUxrCXi5ptjrI+BL/Nio9yOoIsAiv1wYwLK3gik1q9fb3+nTJliN9EgiOq4NsuL1wbbr8VhfdPYsWNjjnFzHkbHQc83eOCGPx3C5/bnT/XclNqx9ozSunApXnt7uwU9CxYsSNvnTZ061daknT9/3sr0KJ1cunSp27lz50/fFw6YUhFvvOmYMxGRTKKMlIhIBmHtE0ENW7jteXFxsa2v4abfr496+PChe//+vdu+fbsrKiqypg/xMgw0Urh//75lr06dOmWBlcd6KFp+RyIRC+TCm1/DE0Zmg8Du2bNnnV7vmyYkg8YJZN7COu6TiSGo+VNoMsG8kpVjnZbfVq1aZcdSuRYaQLS1tSW8FhDolpeXu6NHj1qbcd+wwmeckrlWsouMlyDaY70Z2SffjEJERJKjQEpEJIMQJNGOmxt6n5ECj2tqaizT5AMpyvm4Cd+7d68FNnSuo9FBvK5z48aNs3I8btZnzpwZPTZx4kRXWFhoXd4uXbpkzSrIfNEoItyKPYxGD9u2bXN79uxxjx8/tiCCDnA0NUjW8uXLLTvDe548eWLXRqAYbk3OuCkxZC7o0hcOVH4XTTroeEgDjKFDh8ZsdDCklLKxsTGpcxGoktmhex6ldxcvXoxmmvz10Jzj9OnT7unTp3ZeGkcQFCEvL8868F24cMG9ffvWGl4kQhBMCSEBGcExGUrmko6DBLkiIpI8BVIiIhmEIInOfGR4wjfGBFKs2/Ft0n2Gg5bgrOkZPHiwZaYSlYpxA04mg5bh3LR73OgT0JCZqaiosHVZc+fOtTK0RDfmBBq0Pyd4Yt0NY2McqWSkWNfDjxATSLHmhyBi5cqVMeuMZs2aZWWIzAnXWltb69KFoJNsHvPREQEOW7JZKdZnnTlzxgI+1kARhBI4wV8PAe+GDRtsLRVzTRkk5YMgG0hQSjBJto+W94nQ7p1AjQ6Fo0ePtt++okxw3759vzgTIiL/X1l0nPjbgxAREfldrEmiXDFe+/HfRXZrxYoVtv0bWLNGYEp2KRy4/tcREDNH/LaYiEimU7MJERHpksie8dtNNFSgrI/foQr/sG+60VCiqqrKvXr1yuXk5KT13EeOHLFuivn5+Zb547P4nauuFETRfZHf+QpnBUVEMpkCKRER6ZJonLFjxw4rWSQIobyNssE/gbbvrIsCbeLTjYYdlPPxl9JL2suHf2C4K6A0MV73RRGRTKXSPhERERERkRSp2YSIiIiIiEiKFEiJiIiIiIikSIGUiIiIiIhIihRIiYiIiIiIpEiBlIiIiIiISIoUSImIiIiIiKRIgZSIiIiIiEiKFEiJiIiIiIi41PwDc8Ii1Q69VZEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "wave = pipe.telescope.wave_seq\n", @@ -406,14 +541,7 @@ "#print(spectra.shape)\n", "\n", "plt.figure(figsize=(10, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"Rubix\")\n", - "plt.xlabel(\"Wavelength [Angstrom]\")\n", - "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "#plt.plot(wave, spectra[12,12,:])\n", - "#plt.plot(wave, spectra[8,12,:])\n", "\n", - "plt.subplot(1, 2, 2)\n", "plt.title(\"Rubix Sharded\")\n", "plt.xlabel(\"Wavelength [Angstrom]\")\n", "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", @@ -432,9 +560,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", @@ -447,17 +586,12 @@ "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", "\n", "# Plot side by side\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - "# Original IFU datacube image\n", - "#im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", - "axes[0].set_title(\"Original IFU Datacube\")\n", - "#fig.colorbar(im0, ax=axes[0])\n", + "fig, axes = plt.subplots(1, 1, figsize=(12, 5))\n", "\n", "# Sharded IFU datacube image\n", - "im1 = axes[1].imshow(sharded_image, origin=\"lower\", cmap=\"inferno\")\n", - "axes[1].set_title(\"Sharded IFU Datacube\")\n", - "fig.colorbar(im1, ax=axes[1])\n", + "im1 = axes.imshow(sharded_image, origin=\"lower\", cmap=\"inferno\")\n", + "axes.set_title(\"Sharded IFU Datacube\")\n", + "fig.colorbar(im1, ax=axes)\n", "\n", "plt.tight_layout()\n", "plt.show()" diff --git a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb deleted file mode 100644 index 19dd84a0..00000000 --- a/notebooks/rubix_pipeline_single_function_shard_map_fits.ipynb +++ /dev/null @@ -1,542 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from jax import config\n", - "#config.update(\"jax_enable_x64\", True)\n", - "config.update('jax_num_cpu_devices', 2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import os\n", - "\n", - "# Tell XLA to fake 2 host CPU devices\n", - "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", - "\n", - "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", - "\n", - "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", - "\n", - "import jax\n", - "\n", - "# Now JAX will list two CpuDevice entries\n", - "print(jax.devices())\n", - "# → [CpuDevice(id=0), CpuDevice(id=1)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "#import os\n", - "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RUBIX pipeline\n", - "\n", - "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", - "\n", - "## How to use the Pipeline\n", - "1) Define a `config`\n", - "2) Setup the `pipeline yaml`\n", - "3) Run the RUBIX pipeline\n", - "4) Do science with the mock-data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Config\n", - "\n", - "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", - "\n", - "For the `config` you can choose the following options:\n", - "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", - "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", - "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", - "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", - "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", - "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", - "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", - "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", - "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", - "- `simulation - name`: currently only IllustrisTNG is supported\n", - "- `simulation - args - path`: where the data is stored and how the file will be named\n", - "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", - "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", - "- `telescope - psf`: define the point spread function that is applied to the mock data\n", - "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", - "- `telescope - noise`: define the noise that is applied to the mock data\n", - "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", - "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", - "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", - "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "from rubix.core.pipeline import RubixPipeline \n", - "import os\n", - "\n", - "galaxy_id = \"g7.66e11\"\n", - "\n", - "config_NIHAO = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"NihaoHandler\",\n", - " \"args\": {\n", - " \"particle_type\": [\"stars\"],\n", - " \"save_data_path\": \"data\",\n", - " \"snapshot\": \"1024\",\n", - " },\n", - " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": False, \"subset_size\": 200000},\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"NIHAO\",\n", - " \"args\": {\n", - " \"path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024',\n", - " \"halo_path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024.z0.000.AHF_halos',\n", - " \"halo_id\": 0,\n", - " },\n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE_WFM\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.01,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"BruzualCharlot2003\" #\"Mastar_CB19_SLOG_1_5\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "config_TNG = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"IllustrisAPI\",\n", - " \"args\": {\n", - " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", - " \"particle_type\": [\"stars\"],\n", - " \"simulation\": \"TNG50-1\",\n", - " \"snapshot\": 99,\n", - " \"save_data_path\": \"data\",\n", - " },\n", - " \n", - " \"load_galaxy_args\": {\n", - " \"id\": 12,\n", - " \"reuse\": True,\n", - " },\n", - " \n", - " \"subset\": {\n", - " \"use_subset\": True,\n", - " \"subset_size\": 1000,\n", - " },\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"IllustrisTNG\",\n", - " \"args\": {\n", - " \"path\": \"data/galaxy-id-12.hdf5\",\n", - " },\n", - " \n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.1,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"FSPS\", #\"Mastar_CB19_SLOG_1_5\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Pipeline yaml\n", - "\n", - "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", - "\n", - "```yaml\n", - "calc_ifu:\n", - " Transformers:\n", - " rotate_galaxy:\n", - " name: rotate_galaxy\n", - " depends_on: null\n", - " args: []\n", - " kwargs:\n", - " type: \"face-on\"\n", - " filter_particles:\n", - " name: filter_particles\n", - " depends_on: rotate_galaxy\n", - " args: []\n", - " kwargs: {}\n", - " spaxel_assignment:\n", - " name: spaxel_assignment\n", - " depends_on: filter_particles\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " reshape_data:\n", - " name: reshape_data\n", - " depends_on: spaxel_assignment\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " calculate_spectra:\n", - " name: calculate_spectra\n", - " depends_on: reshape_data\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " scale_spectrum_by_mass:\n", - " name: scale_spectrum_by_mass\n", - " depends_on: calculate_spectra\n", - " args: []\n", - " kwargs: {}\n", - " doppler_shift_and_resampling:\n", - " name: doppler_shift_and_resampling\n", - " depends_on: scale_spectrum_by_mass\n", - " args: []\n", - " kwargs: {}\n", - " calculate_datacube:\n", - " name: calculate_datacube\n", - " depends_on: doppler_shift_and_resampling\n", - " args: []\n", - " kwargs: {}\n", - " convolve_psf:\n", - " name: convolve_psf\n", - " depends_on: calculate_datacube\n", - " args: []\n", - " kwargs: {}\n", - " convolve_lsf:\n", - " name: convolve_lsf\n", - " depends_on: convolve_psf\n", - " args: []\n", - " kwargs: {}\n", - " apply_noise:\n", - " name: apply_noise\n", - " depends_on: convolve_lsf\n", - " args: []\n", - " kwargs: {}\n", - "```\n", - "\n", - "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Run the pipeline\n", - "\n", - "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config_TNG)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "\n", - "inputdata = pipe.prepare_data()\n", - "rubixdata = pipe.run_sharded(inputdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#print(rubixdata)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert luminosity to flux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from rubix.spectra.ifu import convert_luminoisty_to_flux\n", - "from rubix.cosmology import PLANCK15\n", - "\n", - "observation_lum_dist = PLANCK15.luminosity_distance_to_z(config_NIHAO[\"galaxy\"][\"dist_z\"])\n", - "observation_z = config_NIHAO[\"galaxy\"][\"dist_z\"]\n", - "pixel_size = 1.0\n", - "fluxcube = convert_luminoisty_to_flux(rubixdata, observation_lum_dist, observation_z, pixel_size)\n", - "rubixdata = fluxcube/1e-20" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Store datacube in a fits file with header" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "#from rubix.core.fits import store_fits\n", - "\n", - "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_ultraWFM\":\n", - "# cutted_datatcube = data.stars.datacube[300:600, :, :]\n", - "# data.stars.datacube = cutted_datatcube\n", - "#if config_illustris[\"telescope\"][\"name\"] == \"MUSE_WFM\":\n", - "# cutted_datatcube = data.stars.datacube[100:200, :, :]\n", - "# data.stars.datacube = cutted_datatcube\n", - "\n", - "#store_fits(config_NIHAO, rubixdata, \"./output/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Mock-data\n", - "\n", - "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import jax.numpy as jnp\n", - "\n", - "wave = pipe.telescope.wave_seq\n", - "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", - "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "wave = pipe.telescope.wave_seq\n", - "\n", - "#spectra = rubixdata#.stars.datacube # Spectra of all stars\n", - "spectra_sharded = rubixdata # Spectra of all stars\n", - "#print(spectra.shape)\n", - "\n", - "plt.figure(figsize=(10, 5))\n", - "#plt.subplot(1, 2, 1)\n", - "#plt.title(\"Rubix\")\n", - "#plt.xlabel(\"Wavelength [Angstrom]\")\n", - "#plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "#plt.plot(wave, spectra[12,12,:])\n", - "#plt.plot(wave, spectra[8,12,:])\n", - "\n", - "#plt.subplot(1, 2, 2)\n", - "plt.title(\"Rubix Sharded\")\n", - "plt.xlabel(\"Wavelength [Angstrom]\")\n", - "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "plt.plot(wave, spectra_sharded[21,15,:])\n", - "plt.plot(wave, spectra_sharded[15,21,:])\n", - "plt.plot(wave, spectra_sharded[13,4,:])\n", - "plt.plot(wave, spectra_sharded[4,13,:])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot a spacial image of the data cube" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import numpy as np\n", - "# get the spectra of the visible wavelengths from the ifu cube\n", - "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", - "#visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", - "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", - "#visible_spectra.shape\n", - "\n", - "#image = jnp.sum(visible_spectra, axis=2)\n", - "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", - "img32 = np.array(sharded_image, dtype=np.float32)\n", - "\n", - "# Plot side by side\n", - "plt.figure(figsize=(6, 5))\n", - "\n", - "# Original IFU datacube image\n", - "#im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", - "#axes[0].set_title(\"Original IFU Datacube\")\n", - "#fig.colorbar(im0, ax=axes[0])\n", - "\n", - "# Sharded IFU datacube image\n", - "plt.imshow(img32, origin=\"lower\", cmap=\"inferno\", vmin=0, vmax=1e5)\n", - "plt.title(\"Sharded IFU Datacube\")\n", - "plt.colorbar(label=\"Flux [erg/s/cm^2]\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DONE!\n", - "\n", - "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb b/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb deleted file mode 100644 index ec20e063..00000000 --- a/notebooks/rubix_pipeline_single_function_shard_map_memory.ipynb +++ /dev/null @@ -1,517 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "from jax import config\n", - "config.update(\"jax_enable_x64\", True)\n", - "\n", - "# if we're running on CPU, need to pre-specify # cores for explicit parallelism\n", - "# used to have to do import os; os.environ[\"XLA_FLAGS\"] = \"--xla_force_host_platform_device_count=8\"\n", - "config.update('jax_num_cpu_devices', 32)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import os\n", - "\n", - "# Tell XLA to fake 2 host CPU devices\n", - "#os.environ['XLA_FLAGS'] = '--xla_force_host_platform_device_count=3'\n", - "\n", - "# Only make GPU 0 and GPU 1 visible to JAX:\n", - "#os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3,4,5'\n", - "\n", - "#os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"] = \"false\"\n", - "\n", - "import jax\n", - "\n", - "# Now JAX will list two CpuDevice entries\n", - "print(jax.devices())\n", - "# → [CpuDevice(id=0), CpuDevice(id=1)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "#import os\n", - "# os.environ['SPS_HOME'] = '/mnt/storage/annalena_data/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/home/annalena/sps_fsps'\n", - "#os.environ['SPS_HOME'] = '/Users/annalena/Documents/GitHub/fsps'\n", - "#os.environ['SPS_HOME'] = '/export/home/aschaibl/fsps'\n", - "os.environ['SPS_HOME'] = '/home/annalena_data/sps_fsps'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RUBIX pipeline\n", - "\n", - "RUBIX is designed as a linear pipeline, where the individual functions are called and constructed as a pipeline. This allows as to execude the whole data transformation from a cosmological hydrodynamical simulation of a galaxy to an IFU cube in two lines of code. This notebook shows, how to execute the pipeline. To see, how the pipeline is execuded in small individual steps per individual function, we refer to the notebook `rubix_pipeline_stepwise.ipynb`.\n", - "\n", - "## How to use the Pipeline\n", - "1) Define a `config`\n", - "2) Setup the `pipeline yaml`\n", - "3) Run the RUBIX pipeline\n", - "4) Do science with the mock-data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Config\n", - "\n", - "The `config` contains all the information needed to run the pipeline. Those are run specfic configurations. Currently we just support Illustris as simulation, but extensions to other simulations (e.g. NIHAO) are planned.\n", - "\n", - "For the `config` you can choose the following options:\n", - "- `pipeline`: you specify the name of the pipeline that is stored in the yaml file in rubix/config/pipeline_config.yml\n", - "- `logger`: RUBIX has implemented a logger to report the user, what is happening during the pipeline execution and give warnings\n", - "- `data - args - particle_type`: load only stars particle (\"particle_type\": [\"stars\"]) or only gas particle (\"particle_type\": [\"gas\"]) or both (\"particle_type\": [\"stars\",\"gas\"])\n", - "- `data - args - simulation`: choose the Illustris simulation (e.g. \"simulation\": \"TNG50-1\")\n", - "- `data - args - snapshot`: which time step of the simulation (99 for present day)\n", - "- `data - args - save_data_path`: set the path to save the downloaded Illustris data\n", - "- `data - load_galaxy_args - id`: define, which Illustris galaxy is downloaded\n", - "- `data - load_galaxy_args - reuse`: if True, if in th esave_data_path directory a file for this galaxy id already exists, the downloading is skipped and the preexisting file is used\n", - "- `data - subset`: only a defined number of stars/gas particles is used and stored for the pipeline. This may be helpful for quick testing\n", - "- `simulation - name`: currently only IllustrisTNG is supported\n", - "- `simulation - args - path`: where the data is stored and how the file will be named\n", - "- `output_path`: where the hdf5 file is stored, which is then the input to the RUBIX pipeline\n", - "- `telescope - name`: define the telescope instrument that is observing the simulation. Some telescopes are predefined, e.g. MUSE. If your instrument does not exist predefined, you can easily define your instrument in rubix/telescope/telescopes.yaml\n", - "- `telescope - psf`: define the point spread function that is applied to the mock data\n", - "- `telescope - lsf`: define the line spread function that is applied to the mock data\n", - "- `telescope - noise`: define the noise that is applied to the mock data\n", - "- `cosmology`: specify the cosmology you want to use, standard for RUBIX is \"PLANCK15\"\n", - "- `galaxy - dist_z`: specify at which redshift the mock-galaxy is observed\n", - "- `galaxy - rotation`: specify the orientation of the galaxy. You can set the types edge-on or face-on or specify the angles alpha, beta and gamma as rotations around x-, y- and z-axis\n", - "- `ssp - template`: specify the simple stellar population lookup template to get the stellar spectrum for each stars particle. In RUBIX frequently \"BruzualCharlot2003\" is used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import matplotlib.pyplot as plt\n", - "from rubix.core.pipeline import RubixPipeline \n", - "import os\n", - "\n", - "galaxy_id = \"g8.13e11\"\n", - "\n", - "config_NIHAO = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"NihaoHandler\",\n", - " \"args\": {\n", - " \"particle_type\": [\"stars\"],\n", - " \"save_data_path\": \"data\",\n", - " \"snapshot\": \"1024\",\n", - " },\n", - " \"load_galaxy_args\": {\"reuse\": True, \"id\": galaxy_id},\n", - " \"subset\": {\"use_subset\": True, \"subset_size\": 100},\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"NIHAO\",\n", - " \"args\": {\n", - " \"path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024',\n", - " \"halo_path\": f'/home/_data/nihao/nihao_classic/{galaxy_id}/{galaxy_id}.01024.z0.000.AHF_halos',\n", - " \"halo_id\": 0,\n", - " },\n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.1,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"FSPS\" #\"Mastar_CB19_SLOG_1_5\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# NBVAL_SKIP\n", - "config_TNG = {\n", - " \"pipeline\":{\"name\": \"calc_ifu\"},\n", - " \n", - " \"logger\": {\n", - " \"log_level\": \"DEBUG\",\n", - " \"log_file_path\": None,\n", - " \"format\": \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n", - " },\n", - " \"data\": {\n", - " \"name\": \"IllustrisAPI\",\n", - " \"args\": {\n", - " \"api_key\": os.environ.get(\"ILLUSTRIS_API_KEY\"),\n", - " \"particle_type\": [\"stars\"],\n", - " \"simulation\": \"TNG50-1\",\n", - " \"snapshot\": 99,\n", - " \"save_data_path\": \"data\",\n", - " },\n", - " \n", - " \"load_galaxy_args\": {\n", - " \"id\": 14,\n", - " \"reuse\": True,\n", - " },\n", - " \n", - " \"subset\": {\n", - " \"use_subset\": True,\n", - " \"subset_size\": 2000,\n", - " },\n", - " },\n", - " \"simulation\": {\n", - " \"name\": \"IllustrisTNG\",\n", - " \"args\": {\n", - " \"path\": \"data/galaxy-id-14.hdf5\",\n", - " },\n", - " \n", - " },\n", - " \"output_path\": \"output\",\n", - "\n", - " \"telescope\":\n", - " {\"name\": \"MUSE\",\n", - " \"psf\": {\"name\": \"gaussian\", \"size\": 5, \"sigma\": 0.6},\n", - " \"lsf\": {\"sigma\": 0.5},\n", - " \"noise\": {\"signal_to_noise\": 100,\"noise_distribution\": \"normal\"},},\n", - " \"cosmology\":\n", - " {\"name\": \"PLANCK15\"},\n", - " \n", - " \"galaxy\":\n", - " {\"dist_z\": 0.1,\n", - " \"rotation\": {\"type\": \"edge-on\"},\n", - " },\n", - " \n", - " \"ssp\": {\n", - " \"template\": {\n", - " \"name\": \"FSPS\", #\"Mastar_CB19_SLOG_1_5\"\n", - " },\n", - " \"dust\": {\n", - " \"extinction_model\": \"Cardelli89\",\n", - " \"dust_to_gas_ratio\": 0.01,\n", - " \"dust_to_metals_ratio\": 0.4,\n", - " \"dust_grain_density\": 3.5,\n", - " \"Rv\": 3.1,\n", - " },\n", - " }, \n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Pipeline yaml\n", - "\n", - "To run the RUBIX pipeline, you need a yaml file (stored in `rubix/config/pipeline_config.yml`) that defines which functions are used during the execution of the pipeline. This shows the example pipeline yaml to compute a stellar IFU cube.\n", - "\n", - "```yaml\n", - "calc_ifu:\n", - " Transformers:\n", - " rotate_galaxy:\n", - " name: rotate_galaxy\n", - " depends_on: null\n", - " args: []\n", - " kwargs:\n", - " type: \"face-on\"\n", - " filter_particles:\n", - " name: filter_particles\n", - " depends_on: rotate_galaxy\n", - " args: []\n", - " kwargs: {}\n", - " spaxel_assignment:\n", - " name: spaxel_assignment\n", - " depends_on: filter_particles\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " reshape_data:\n", - " name: reshape_data\n", - " depends_on: spaxel_assignment\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " calculate_spectra:\n", - " name: calculate_spectra\n", - " depends_on: reshape_data\n", - " args: []\n", - " kwargs: {}\n", - "\n", - " scale_spectrum_by_mass:\n", - " name: scale_spectrum_by_mass\n", - " depends_on: calculate_spectra\n", - " args: []\n", - " kwargs: {}\n", - " doppler_shift_and_resampling:\n", - " name: doppler_shift_and_resampling\n", - " depends_on: scale_spectrum_by_mass\n", - " args: []\n", - " kwargs: {}\n", - " calculate_datacube:\n", - " name: calculate_datacube\n", - " depends_on: doppler_shift_and_resampling\n", - " args: []\n", - " kwargs: {}\n", - " convolve_psf:\n", - " name: convolve_psf\n", - " depends_on: calculate_datacube\n", - " args: []\n", - " kwargs: {}\n", - " convolve_lsf:\n", - " name: convolve_lsf\n", - " depends_on: convolve_psf\n", - " args: []\n", - " kwargs: {}\n", - " apply_noise:\n", - " name: apply_noise\n", - " depends_on: convolve_lsf\n", - " args: []\n", - " kwargs: {}\n", - "```\n", - "\n", - "Ther is one thing you have to know about the naming of the functions in this yaml: To use the functions inside the pipeline, the functions have to be called exactly the same as they are returned from the core module function!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Run the pipeline\n", - "\n", - "After defining the `config` and the `pipeline_config` you can simply run the whole pipeline by these two lines of code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "pipe = RubixPipeline(config_NIHAO)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "\n", - "inputdata = pipe.prepare_data()\n", - "rubixdata = pipe.run_sharded(inputdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "config_NIHAO[\"pipeline\"][\"name\"] = \"calc_ifu\"\n", - "pipe = RubixPipeline(config_NIHAO)\n", - "\n", - "inputdata = pipe.prepare_data()\n", - "rubixdata_old = pipe.run_sharded(inputdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#print(rubixdata)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "\n", - "#inputdata = pipe.prepare_data()\n", - "#shard_rubixdata = pipe.run_sharded_chunked(inputdata)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Mock-data\n", - "\n", - "Now we have our final datacube and can use the mock-data to do science. Here we have a quick look in the optical wavelengthrange of the mock-datacube and show the spectra of a central spaxel and a spatial image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "import jax.numpy as jnp\n", - "\n", - "wave = pipe.telescope.wave_seq\n", - "# get the indices of the visible wavelengths of 4000-8000 Angstroms\n", - "visible_indices = jnp.where((wave >= 4000) & (wave <= 8000))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is how you can access the spectrum of an individual spaxel, the wavelength can be accessed via `pipe.wave_seq`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "wave = pipe.telescope.wave_seq\n", - "\n", - "spectra = rubixdata_old#.stars.datacube # Spectra of all stars\n", - "spectra_sharded = rubixdata # Spectra of all stars\n", - "#print(spectra.shape)\n", - "\n", - "plt.figure(figsize=(10, 5))\n", - "plt.subplot(1, 2, 1)\n", - "plt.title(\"Rubix\")\n", - "plt.xlabel(\"Wavelength [Angstrom]\")\n", - "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "plt.plot(wave, spectra[12,12,:])\n", - "plt.plot(wave, spectra[8,12,:])\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.title(\"Rubix Sharded\")\n", - "plt.xlabel(\"Wavelength [Angstrom]\")\n", - "plt.ylabel(\"Flux [erg/s/cm^2/Angstrom]\")\n", - "plt.plot(wave, spectra_sharded[12,12,:])\n", - "plt.plot(wave, spectra_sharded[8,12,:])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot a spacial image of the data cube" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#NBVAL_SKIP\n", - "# get the spectra of the visible wavelengths from the ifu cube\n", - "#visible_spectra = rubixdata.stars.datacube[ :, :, visible_indices[0]]\n", - "visible_spectra = rubixdata_old[ :, :, visible_indices[0]]\n", - "sharded_visible_spectra = rubixdata[ :, :, visible_indices[0]]\n", - "#visible_spectra.shape\n", - "\n", - "image = jnp.sum(visible_spectra, axis=2)\n", - "sharded_image = jnp.sum(sharded_visible_spectra, axis=2)\n", - "\n", - "# Plot side by side\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - "# Original IFU datacube image\n", - "im0 = axes[0].imshow(image, origin=\"lower\", cmap=\"inferno\")\n", - "axes[0].set_title(\"Original IFU Datacube\")\n", - "fig.colorbar(im0, ax=axes[0])\n", - "\n", - "# Sharded IFU datacube image\n", - "im1 = axes[1].imshow(sharded_image, origin=\"lower\", cmap=\"inferno\")\n", - "axes[1].set_title(\"Sharded IFU Datacube\")\n", - "fig.colorbar(im1, ax=axes[1])\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DONE!\n", - "\n", - "Congratulations, you have sucessfully run the RUBIX pipeline to create your own mock-observed IFU datacube! Now enjoy playing around with the RUBIX pipeline and enjoy doing amazing science with RUBIX :)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rubix", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/rubix/core/data.py b/rubix/core/data.py index 361c96f4..7c390fc8 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -2,7 +2,7 @@ import os from dataclasses import dataclass from functools import partial -from typing import Callable, Optional, Union +from typing import Callable, Optional, Union, Any import jax import jax.numpy as jnp @@ -17,7 +17,7 @@ # Registering the dataclass with JAX for automatic tree traversal -# @jaxtyped(typechecker=typechecker) +@jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class Galaxy: @@ -30,9 +30,22 @@ class Galaxy: halfmassrad_stars: Half mass radius of the stars in the galaxy """ - redshift: Optional[jnp.ndarray] = None - center: Optional[jnp.ndarray] = None - halfmassrad_stars: Optional[jnp.ndarray] = None + redshift: Optional[Any] = None + center: Optional[Any] = None + halfmassrad_stars: Optional[Any] = None + + def __repr__(self): + representationString = ["Galaxy:"] + for k, v in self.__dict__.items(): + if not k.endswith("_unit"): + if v is not None: + attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + attrString += f", unit = {getattr(self, k + '_unit')}" + representationString.append(attrString) + else: + representationString.append(f"{k}: None") + return "\n\t".join(representationString) def tree_flatten(self): """ @@ -62,7 +75,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -# @jaxtyped(typechecker=typechecker) +@jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class StarsData: @@ -83,16 +96,29 @@ class StarsData: """ - coords: Optional[jnp.ndarray] = None - velocity: Optional[jnp.ndarray] = None - mass: Optional[jnp.ndarray] = None - metallicity: Optional[jnp.ndarray] = None - age: Optional[jnp.ndarray] = None - pixel_assignment: Optional[jnp.ndarray] = None - spatial_bin_edges: Optional[jnp.ndarray] = None - mask: Optional[jnp.ndarray] = None - spectra: Optional[jnp.ndarray] = None - datacube: Optional[jnp.ndarray] = None + coords: Optional[Any] = None + velocity: Optional[Any] = None + mass: Optional[Any] = None + metallicity: Optional[Any] = None + age: Optional[Any] = None + pixel_assignment: Optional[Any] = None + spatial_bin_edges: Optional[Any] = None + mask: Optional[Any] = None + spectra: Optional[Any] = None + datacube: Optional[Any] = None + + def __repr__(self): + representationString = ["StarsData:"] + for k, v in self.__dict__.items(): + if not k.endswith("_unit"): + if v is not None: + attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + attrString += f", unit = {getattr(self, k + '_unit')}" + representationString.append(attrString) + else: + representationString.append(f"{k}: None") + return "\n\t".join(representationString) def tree_flatten(self): """ @@ -133,7 +159,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -# @jaxtyped(typechecker=typechecker) +@jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class GasData: @@ -156,20 +182,33 @@ class GasData: datacube: IFU datacube for the gas component """ - coords: Optional[jnp.ndarray] = None - velocity: Optional[jnp.ndarray] = None - mass: Optional[jnp.ndarray] = None - density: Optional[jnp.ndarray] = None - internal_energy: Optional[jnp.ndarray] = None - metallicity: Optional[jnp.ndarray] = None - metals: Optional[jnp.ndarray] = None - sfr: Optional[jnp.ndarray] = None - electron_abundance: Optional[jnp.ndarray] = None - pixel_assignment: Optional[jnp.ndarray] = None - spatial_bin_edges: Optional[jnp.ndarray] = None - mask: Optional[jnp.ndarray] = None - spectra: Optional[jnp.ndarray] = None - datacube: Optional[jnp.ndarray] = None + coords: Optional[Any] = None + velocity: Optional[Any] = None + mass: Optional[Any] = None + density: Optional[Any] = None + internal_energy: Optional[Any] = None + metallicity: Optional[Any] = None + metals: Optional[Any] = None + sfr: Optional[Any] = None + electron_abundance: Optional[Any] = None + pixel_assignment: Optional[Any] = None + spatial_bin_edges: Optional[Any] = None + mask: Optional[Any] = None + spectra: Optional[Any] = None + datacube: Optional[Any] = None + + def __repr__(self): + representationString = ["GasData:"] + for k, v in self.__dict__.items(): + if not k.endswith("_unit"): + if v is not None: + attrString = f"{k}: shape = {v.shape}, dtype = {v.dtype}" + if hasattr(self, k + "_unit") and getattr(self, k + "_unit") != "": + attrString += f", unit = {getattr(self, k + '_unit')}" + representationString.append(attrString) + else: + representationString.append(f"{k}: None") + return "\n\t".join(representationString) def tree_flatten(self): """ @@ -214,7 +253,7 @@ def tree_unflatten(cls, aux_data, children): return cls(*children) -# @jaxtyped(typechecker=typechecker) +@jaxtyped(typechecker=typechecker) @partial(jax.tree_util.register_pytree_node_class) @dataclass class RubixData: @@ -231,6 +270,12 @@ class RubixData: stars: Optional[StarsData] = None gas: Optional[GasData] = None + def __repr__(self): + representationString = ["RubixData:"] + for k, v in self.__dict__.items(): + representationString.append("\n\t".join(f"{k}: {v}".split("\n"))) + return "\n\t".join(representationString) + def tree_flatten(self): """ Flattens the RubixData object into a tuple of children and auxiliary data @@ -331,15 +376,19 @@ def convert_to_rubix(config: Union[dict, str]): # If the simulationtype is IllustrisAPI, get data from IllustrisAPI # TODO: we can do this more elgantly + if "data" in config: if config["data"]["name"] == "IllustrisAPI": logger.info("Loading data from IllustrisAPI") api = IllustrisAPI(**config["data"]["args"], logger=logger) api.load_galaxy(**config["data"]["load_galaxy_args"]) - # else: - # raise ValueError(f"Unknown data source: {config['data']['name']}.") + elif config["data"]["name"] == "NihaoHandler": + logger.info("Loading data from Nihao simulation") + else: + raise ValueError(f"Unknown data source: {config['data']['name']}.") + - # Load the saved data into the input handler + # Load the saved data into the input handler logger.info("Loading data into input handler") input_handler = get_input_handler(config, logger=logger) input_handler.to_rubix(output_path=config["output_path"]) From 85f743f07f91fcc8f53ff878c1c82abcb4b341a1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 4 Nov 2025 10:15:25 +0000 Subject: [PATCH 71/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- ...x_pipeline_single_function_shard_map.ipynb | 180 ++---------------- rubix/core/data.py | 3 +- 2 files changed, 18 insertions(+), 165 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 11d76711..53f4ac9e 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -20,17 +20,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -52,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -113,26 +105,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-04 11:13:09,119 - rubix - INFO - \n", - " ___ __ _____ _____ __\n", - " / _ \\/ / / / _ )/ _/ |/_/\n", - " / , _/ /_/ / _ |/ /_> <\n", - "/_/|_|\\____/____/___/_/|_|\n", - "\n", - "\n", - "2025-11-04 11:13:09,120 - rubix - INFO - Rubix version: 0.0.post503+g060c53b49.d20251002\n", - "2025-11-04 11:13:09,120 - rubix - INFO - JAX version: 0.4.38\n", - "2025-11-04 11:13:09,120 - rubix - INFO - Running on [CpuDevice(id=0)] devices\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import matplotlib.pyplot as plt\n", @@ -199,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -355,18 +330,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "pipe = RubixPipeline(config_TNG)" @@ -374,99 +340,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-04 11:13:09,998 - rubix - INFO - Getting rubix data...\n", - "2025-11-04 11:13:09,999 - rubix - INFO - Loading data from IllustrisAPI\n", - "2025-11-04 11:13:10,000 - rubix - INFO - Reusing existing file galaxy-id-12.hdf5. If you want to download the data again, set reuse=False.\n", - "2025-11-04 11:13:10,021 - rubix - INFO - Loading data into input handler\n", - "2025-11-04 11:13:10,022 - rubix - DEBUG - Loading data from Illustris file..\n", - "2025-11-04 11:13:10,022 - rubix - DEBUG - Checking if the fields are present in the file...\n", - "2025-11-04 11:13:10,023 - rubix - DEBUG - Keys in the file: \n", - "2025-11-04 11:13:10,023 - rubix - DEBUG - Expected fields: ['Header', 'SubhaloData', 'PartType4', 'PartType0']\n", - "2025-11-04 11:13:10,023 - rubix - DEBUG - Matching fields: {'Header', 'SubhaloData', 'PartType4'}\n", - "2025-11-04 11:13:10,026 - rubix - DEBUG - Found 649384 valid particles out of 649384\n", - "2025-11-04 11:13:10,233 - rubix - DEBUG - Converting Stellar Formation Time to Age\n", - "2025-11-04 11:13:14,951 - rubix - DEBUG - Converting to Rubix format..\n", - "2025-11-04 11:13:14,951 - rubix - DEBUG - Checking if the fields are present in the particle data...\n", - "2025-11-04 11:13:14,951 - rubix - DEBUG - Keys in the particle data: dict_keys(['stars'])\n", - "2025-11-04 11:13:14,951 - rubix - DEBUG - Expected fields: {'PartType4': 'stars', 'PartType0': 'gas'}\n", - "2025-11-04 11:13:14,952 - rubix - DEBUG - Matching fields: {'stars'}\n", - "2025-11-04 11:13:14,952 - rubix - DEBUG - Required fields for stars: ['coords', 'mass', 'metallicity', 'velocity', 'age']\n", - "2025-11-04 11:13:14,952 - rubix - DEBUG - Available fields in particle_data[stars]: ['coords', 'mass', 'metallicity', 'age', 'velocity']\n", - "2025-11-04 11:13:14,952 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-11-04 11:13:14,953 - rubix - DEBUG - Creating Rubix file at path: output/rubix_galaxy.h5\n", - "2025-11-04 11:13:14,956 - rubix - DEBUG - Converting redshift for galaxy data into \n", - "2025-11-04 11:13:14,957 - rubix - DEBUG - Converting center for galaxy data into kpc\n", - "2025-11-04 11:13:14,957 - rubix - DEBUG - Converting halfmassrad_stars for galaxy data into kpc\n", - "2025-11-04 11:13:14,958 - rubix - DEBUG - Converting coords for particle type stars into kpc\n", - "2025-11-04 11:13:14,969 - rubix - DEBUG - Converting mass for particle type stars into Msun\n", - "2025-11-04 11:13:15,004 - rubix - DEBUG - Converting metallicity for particle type stars into \n", - "2025-11-04 11:13:15,008 - rubix - DEBUG - Converting age for particle type stars into Gyr\n", - "2025-11-04 11:13:15,019 - rubix - DEBUG - Converting velocity for particle type stars into km/s\n", - "2025-11-04 11:13:15,028 - rubix - INFO - Rubix file saved at output/rubix_galaxy.h5\n", - "2025-11-04 11:13:15,074 - rubix - INFO - Centering stars particles\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Converted to Rubix format!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-11-04 11:13:15,657 - rubix - WARNING - The Subset value is set in config. Using only subset of size 2000 for stars\n", - "2025-11-04 11:13:15,658 - rubix - INFO - Data loaded with 2000 star particles and 0 gas particles.\n", - "2025-11-04 11:13:15,659 - rubix - INFO - Data preparation completed in 5.66 seconds.\n", - "2025-11-04 11:13:15,659 - rubix - INFO - Setting up the pipeline...\n", - "2025-11-04 11:13:15,659 - rubix - DEBUG - Pipeline Configuration: {'Transformers': {'rotate_galaxy': {'name': 'rotate_galaxy', 'depends_on': None, 'args': [], 'kwargs': {}}, 'filter_particles': {'name': 'filter_particles', 'depends_on': 'rotate_galaxy', 'args': [], 'kwargs': {}}, 'spaxel_assignment': {'name': 'spaxel_assignment', 'depends_on': 'filter_particles', 'args': [], 'kwargs': {}}, 'calculate_datacube_particlewise': {'name': 'calculate_datacube_particlewise', 'depends_on': 'spaxel_assignment', 'args': [], 'kwargs': {}}, 'convolve_psf': {'name': 'convolve_psf', 'depends_on': 'calculate_datacube_particlewise', 'args': [], 'kwargs': {}}, 'convolve_lsf': {'name': 'convolve_lsf', 'depends_on': 'convolve_psf', 'args': [], 'kwargs': {}}, 'apply_noise': {'name': 'apply_noise', 'depends_on': 'convolve_lsf', 'args': [], 'kwargs': {}}}}\n", - "2025-11-04 11:13:15,660 - rubix - DEBUG - Rotation Type found: edge-on\n", - "2025-11-04 11:13:15,661 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-11-04 11:13:15,669 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-11-04 11:13:15,812 - rubix - INFO - Calculating spatial bin edges...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-11-04 11:13:15,819 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-11-04 11:13:15,826 - rubix - INFO - Getting cosmology...\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-11-04 11:13:16,084 - rubix - DEBUG - Method not defined, using default method: cubic\n", - "/Users/annalena/Documents/GitHub/rubix/rubix/telescope/factory.py:26: UserWarning: No telescope config provided, using default stored in /Users/annalena/Documents/GitHub/rubix/rubix/telescope/telescopes.yaml\n", - " warnings.warn(\n", - "2025-11-04 11:13:16,489 - rubix - INFO - Assembling the pipeline...\n", - "2025-11-04 11:13:16,490 - rubix - INFO - Compiling the expressions...\n", - "2025-11-04 11:13:16,490 - rubix - INFO - Number of devices: 1\n", - "2025-11-04 11:13:16,610 - rubix - INFO - Rotating galaxy with alpha=90.0, beta=0.0, gamma=0.0\n", - "2025-11-04 11:13:16,611 - rubix - INFO - Rotating galaxy for simulation: IllustrisTNG\n", - "2025-11-04 11:13:16,611 - rubix - WARNING - Gas not found in particle_type, only rotating stellar component.\n", - "2025-11-04 11:13:16,653 - rubix - INFO - Filtering particles outside the aperture...\n", - "2025-11-04 11:13:16,655 - rubix - INFO - Assigning particles to spaxels...\n", - "2025-11-04 11:13:16,663 - rubix - INFO - Calculating Data Cube (combined per‐particle)…\n", - "2025-11-04 11:13:16,810 - rubix - DEBUG - Datacube shape: (25, 25, 3721)\n", - "2025-11-04 11:13:16,811 - rubix - INFO - Convolving with PSF...\n", - "2025-11-04 11:13:16,812 - rubix - INFO - Convolving with LSF...\n", - "2025-11-04 11:13:16,814 - rubix - INFO - Applying noise to datacube with signal to noise ratio: 100 and noise distribution: normal\n", - "2025-11-04 11:13:17,748 - rubix - INFO - Sharding completed in 0.84 seconds.\n", - "2025-11-04 11:13:17,749 - rubix - INFO - Sharded pipeline run completed in 1.25 seconds.\n", - "2025-11-04 11:13:17,749 - rubix - INFO - Total time for sharded pipeline run: 2.09 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "\n", @@ -476,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -497,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -518,20 +394,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "# get the spectra of the visible wavelengths from the ifu cube\n", diff --git a/rubix/core/data.py b/rubix/core/data.py index 7c390fc8..ce258989 100644 --- a/rubix/core/data.py +++ b/rubix/core/data.py @@ -2,7 +2,7 @@ import os from dataclasses import dataclass from functools import partial -from typing import Callable, Optional, Union, Any +from typing import Any, Callable, Optional, Union import jax import jax.numpy as jnp @@ -387,7 +387,6 @@ def convert_to_rubix(config: Union[dict, str]): else: raise ValueError(f"Unknown data source: {config['data']['name']}.") - # Load the saved data into the input handler logger.info("Loading data into input handler") input_handler = get_input_handler(config, logger=logger) From 6fe3212aab2dc9e4ab5b40b371c211842a3418e7 Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 4 Nov 2025 11:33:39 +0100 Subject: [PATCH 72/76] change core modules regarding comments --- rubix/core/ifu.py | 23 ++++++++++++++--------- rubix/core/pipeline.py | 22 ++++++++++++++-------- 2 files changed, 28 insertions(+), 17 deletions(-) diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 4f52b3cb..6173f739 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -23,14 +23,20 @@ @jaxtyped(typechecker=typechecker) def get_calculate_datacube_particlewise(config: dict) -> Callable: """ - Returns a function that builds the IFU cube by, for each star: - 1) looking up SSP - 2) scaling by mass - 3) Doppler‐shifting - 4) resampling - 5) accumulating into the shared datacube - - Args + Create a function that calculates the datacube for the stars component + of a RubixData object on a per-particle basis. First, it looks up the SSP + spectrum for each star based on its age and metallicity, scales it by the + star's mass, applies a Doppler shift based on the star's velocity, resamples + the spectrum onto the telescope's wavelength grid, and finally accumulates + the resulting spectra into the appropriate pixels of the datacube. + + Args: + config (dict): Configuration dictionary containing telescope and galaxy + parameters. + + Returns: + Callable: A function that takes a RubixData object and returns it with + the datacube calculated and added to the stars component. """ logger = get_logger(config.get("logger", None)) telescope = get_telescope(config) @@ -99,5 +105,4 @@ def body(cube, i): logger.debug(f"Datacube shape: {cube_3d.shape}") return rubixdata - # return jax.jit(calculate_datacube_particlewise) return calculate_datacube_particlewise diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 9e9e0db4..81d9b036 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -54,6 +54,20 @@ class RubixPipeline: """ def __init__(self, user_config: Union[dict, str]): + """ + Initializes the RubixPipeline with the given user configuration. + + Args: + user_config (Union[dict, str]): User configuration dictionary or path to config file. + pipeline_config (dict): Pipeline configuration dictionary. + logger: Logger instance for logging messages. + ssp: SSP model instance. + telescope: Telescope instance. + func: Compiled pipeline function. + + Returns: + None + """ self.user_config = get_config(user_config) self.pipeline_config = get_pipeline_config(self.user_config["pipeline"]["name"]) self.logger = get_logger(self.user_config["logger"]) @@ -97,7 +111,6 @@ def _get_pipeline_functions(self) -> list: rotate_galaxy = get_galaxy_rotation(self.user_config) filter_particles = get_filter_particles(self.user_config) spaxel_assignment = get_spaxel_assignment(self.user_config) - # reshape_data = get_reshape_data(self.user_config) apply_extinction = get_extinction(self.user_config) calculate_datacube_particlewise = get_calculate_datacube_particlewise( self.user_config @@ -110,7 +123,6 @@ def _get_pipeline_functions(self) -> list: rotate_galaxy, filter_particles, spaxel_assignment, - # reshape_data, apply_extinction, calculate_datacube_particlewise, convolve_psf, @@ -243,18 +255,12 @@ def _shard_pipeline(sharded_rubixdata): check_rep=False, ) - time_mid = time.time() sharded_result = sharded_pipeline(inputdata) time_end = time.time() - self.logger.info("Sharding completed in %.2f seconds.", time_mid - time_start) - self.logger.info( - "Sharded pipeline run completed in %.2f seconds.", time_end - time_mid - ) self.logger.info( "Total time for sharded pipeline run: %.2f seconds.", time_end - time_start, ) - # final_cube = jnp.sum(partial_cubes, axis=0) return sharded_result From f884b84020ba5c0c02f4e0adb6d8326605bd3369 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 4 Nov 2025 10:34:29 +0000 Subject: [PATCH 73/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rubix/core/ifu.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rubix/core/ifu.py b/rubix/core/ifu.py index 6173f739..daa13b22 100644 --- a/rubix/core/ifu.py +++ b/rubix/core/ifu.py @@ -29,7 +29,7 @@ def get_calculate_datacube_particlewise(config: dict) -> Callable: star's mass, applies a Doppler shift based on the star's velocity, resamples the spectrum onto the telescope's wavelength grid, and finally accumulates the resulting spectra into the appropriate pixels of the datacube. - + Args: config (dict): Configuration dictionary containing telescope and galaxy parameters. From 3935dd715bac6577dc18f2f57968ca8364bcc419 Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 4 Nov 2025 11:47:19 +0100 Subject: [PATCH 74/76] changes according to Haralds review comments --- ...x_pipeline_single_function_shard_map.ipynb | 15 ++++- rubix/core/pipeline.py | 4 +- rubix/core/rotation.py | 55 ++----------------- rubix/galaxy/input_handler/pynbody.py | 9 +-- rubix/utils.py | 7 +++ tests/test_core_pipeline.py | 2 - tests/test_pynbody_handler.py | 12 ---- 7 files changed, 32 insertions(+), 72 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index 53f4ac9e..a8cef459 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -20,9 +20,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CpuDevice(id=0)]\n" + ] + } + ], "source": [ "#NBVAL_SKIP\n", "import os\n", @@ -346,8 +354,9 @@ "source": [ "#NBVAL_SKIP\n", "\n", + "devices = jax.devices()\n", "inputdata = pipe.prepare_data()\n", - "rubixdata = pipe.run_sharded(inputdata)" + "rubixdata = pipe.run_sharded(inputdata, devices)" ] }, { diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 81d9b036..335a44c7 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -131,7 +131,7 @@ def _get_pipeline_functions(self) -> list: ] return functions - def run_sharded(self, inputdata): + def run_sharded(self, inputdata, devices): """ Runs the pipeline on sharded input data in parallel using jax.shard_map. It splits the particle arrays (e.g. under stars and gas) into shards, runs @@ -162,7 +162,7 @@ def run_sharded(self, inputdata): self.logger.info("Compiling the expressions...") self.func = self._pipeline.compile_expression() - devices = jax.devices() + #devices = jax.devices() num_devices = len(devices) self.logger.info("Number of devices: %d", num_devices) diff --git a/rubix/core/rotation.py b/rubix/core/rotation.py index 69666ec3..d21b6481 100644 --- a/rubix/core/rotation.py +++ b/rubix/core/rotation.py @@ -76,57 +76,14 @@ def get_galaxy_rotation(config: dict): @jaxtyped(typechecker=typechecker) def rotate_galaxy(rubixdata: RubixData) -> RubixData: logger.info(f"Rotating galaxy with alpha={alpha}, beta={beta}, gamma={gamma}") - """ - for particle_type in ["stars", "gas"]: - if particle_type in config["data"]["args"]["particle_type"]: - # Get the component (either stars or gas) - logger.info(f"Rotating {particle_type}") - component = getattr(rubixdata, particle_type) - - # Get the inputs - coords = component.coords - velocities = component.velocity - masses = component.mass - halfmass_radius = rubixdata.galaxy.halfmassrad_stars - - assert ( - coords is not None - ), f"Coordinates not found for {particle_type}. " - assert ( - velocities is not None - ), f"Velocities not found for {particle_type}. " - assert masses is not None, f"Masses not found for {particle_type}. " - - if config["galaxy"]["rotation"] == "matrix": - - rot_np = jnp.load("./data/rotation_matrix.npy") - rot_jax = jnp.array(rot_np) - logger.info(f"Using rotation matrix from file: {rot_jax}.") - rotation_matrix = rot_jax - else: - rotation_matrix = None - - # Rotate the galaxy - coords, velocities = rotate_galaxy_core( - positions=coords, - velocities=velocities, - positions_stars=rubixdata.stars.coords, - masses_stars=rubixdata.stars.mass, - halfmass_radius=halfmass_radius, - alpha=alpha, - beta=beta, - gamma=gamma, - R=rotation_matrix, - ) - - # Update the inputs - # rubixdata.stars.coords = coords - # rubixdata.stars.velocity = velocities - setattr(component, "coords", coords) - setattr(component, "velocity", velocities) + Rotates the galaxy particle data based on the specified rotation angles. - return rubixdata + Args: + rubixdata (RubixData): The RubixData object containing particle data. + + Returns: + RubixData: The rotated RubixData object. """ logger.info("Rotating galaxy for simulation: " + config["simulation"]["name"]) # Rotate gas diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index fcae628c..abaced55 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -15,12 +15,13 @@ class PynbodyHandler(BaseHandler): def __init__( - self, path, halo_path=None, logger=None, config=None, dist_z=None, halo_id=None + self, path, halo_path=None, rotation_path="./data", logger=None, config=None, dist_z=None, halo_id=None ): """Initialize handler with paths to snapshot and halo files.""" self.metallicity_unit = Zsun self.path = path self.halo_path = halo_path + self.rotation_path = rotation_path self.halo_id = halo_id self.pynbody_config = config or self._load_config() self.logger = logger or self._default_logger() @@ -77,12 +78,12 @@ def load_data(self): pynbody.analysis.angmom.faceon(halo.s) ang_mom_vec = pynbody.analysis.angmom.ang_mom_vec(halo.s) rotation_matrix = pynbody.analysis.angmom.calc_sideon_matrix(ang_mom_vec) - if not os.path.exists("./data"): + if not os.path.exists(self.rotation_path): self.logger.info("Rotation matrix calculated and not saved.") else: - np.save("./data/rotation_matrix.npy", rotation_matrix) + np.save(os.path.join(self.rotation_path, "rotation_matrix.npy"), rotation_matrix) self.logger.info( - "Rotation matrix calculated and saved to '/notebooks/data/rotation_matrix.npy'." + f"Rotation matrix calculated and saved to '{self.rotation_path}/rotation_matrix.npy'." ) self.sim = halo diff --git a/rubix/utils.py b/rubix/utils.py index 26e7d308..7212f26e 100644 --- a/rubix/utils.py +++ b/rubix/utils.py @@ -202,6 +202,13 @@ def _pad_particles(inputdata, pad: int) -> "InputData": """ Pads the particle arrays in inputdata to make their length divisible by num_devices. This is necessary for sharding to work correctly. + + Args: + inputdata (InputData): The input data containing particle arrays. + pad (int): The number of particles to pad. + + Returns: + InputData: The padded input data. """ # pad along the first axis diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index 0c43b82a..d696fe14 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -127,5 +127,3 @@ def test_rubix_pipeline_run_sharded(): # The cube should have nonzero values (sanity check) assert jnp.any(output_cube != 0) - print("run_sharded output shape:", output_cube.shape) - print("run_sharded output sum:", jnp.sum(output_cube)) diff --git a/tests/test_pynbody_handler.py b/tests/test_pynbody_handler.py index f2ac8301..74a4f4f9 100644 --- a/tests/test_pynbody_handler.py +++ b/tests/test_pynbody_handler.py @@ -97,18 +97,6 @@ def dm_getitem(key): @pytest.fixture def handler_with_mock_data(mock_simulation, mock_config): - """ - with patch("pynbody.load", return_value=mock_simulation): - with patch("pynbody.analysis.angmom.faceon", return_value=None): - handler = PynbodyHandler( - path="mock_path", - halo_path="mock_halo_path", - config=mock_config, - dist_z=mock_config["galaxy"]["dist_z"], - halo_id=1, - ) - return handler - """ with ( patch("pynbody.load", return_value=mock_simulation), patch("pynbody.analysis.angmom.faceon", return_value=None), From 42d61297524035216a6427ffedc58f1b83f370e4 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 4 Nov 2025 10:49:06 +0000 Subject: [PATCH 75/76] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../rubix_pipeline_single_function_shard_map.ipynb | 12 ++---------- rubix/core/pipeline.py | 2 +- rubix/core/rotation.py | 2 +- rubix/galaxy/input_handler/pynbody.py | 14 ++++++++++++-- rubix/utils.py | 2 +- tests/test_core_pipeline.py | 1 - 6 files changed, 17 insertions(+), 16 deletions(-) diff --git a/notebooks/rubix_pipeline_single_function_shard_map.ipynb b/notebooks/rubix_pipeline_single_function_shard_map.ipynb index a8cef459..8c3e9895 100644 --- a/notebooks/rubix_pipeline_single_function_shard_map.ipynb +++ b/notebooks/rubix_pipeline_single_function_shard_map.ipynb @@ -20,17 +20,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CpuDevice(id=0)]\n" - ] - } - ], + "outputs": [], "source": [ "#NBVAL_SKIP\n", "import os\n", diff --git a/rubix/core/pipeline.py b/rubix/core/pipeline.py index 335a44c7..3491d4c7 100644 --- a/rubix/core/pipeline.py +++ b/rubix/core/pipeline.py @@ -162,7 +162,7 @@ def run_sharded(self, inputdata, devices): self.logger.info("Compiling the expressions...") self.func = self._pipeline.compile_expression() - #devices = jax.devices() + # devices = jax.devices() num_devices = len(devices) self.logger.info("Number of devices: %d", num_devices) diff --git a/rubix/core/rotation.py b/rubix/core/rotation.py index d21b6481..bb4024c8 100644 --- a/rubix/core/rotation.py +++ b/rubix/core/rotation.py @@ -81,7 +81,7 @@ def rotate_galaxy(rubixdata: RubixData) -> RubixData: Args: rubixdata (RubixData): The RubixData object containing particle data. - + Returns: RubixData: The rotated RubixData object. """ diff --git a/rubix/galaxy/input_handler/pynbody.py b/rubix/galaxy/input_handler/pynbody.py index abaced55..9decf28d 100644 --- a/rubix/galaxy/input_handler/pynbody.py +++ b/rubix/galaxy/input_handler/pynbody.py @@ -15,7 +15,14 @@ class PynbodyHandler(BaseHandler): def __init__( - self, path, halo_path=None, rotation_path="./data", logger=None, config=None, dist_z=None, halo_id=None + self, + path, + halo_path=None, + rotation_path="./data", + logger=None, + config=None, + dist_z=None, + halo_id=None, ): """Initialize handler with paths to snapshot and halo files.""" self.metallicity_unit = Zsun @@ -81,7 +88,10 @@ def load_data(self): if not os.path.exists(self.rotation_path): self.logger.info("Rotation matrix calculated and not saved.") else: - np.save(os.path.join(self.rotation_path, "rotation_matrix.npy"), rotation_matrix) + np.save( + os.path.join(self.rotation_path, "rotation_matrix.npy"), + rotation_matrix, + ) self.logger.info( f"Rotation matrix calculated and saved to '{self.rotation_path}/rotation_matrix.npy'." ) diff --git a/rubix/utils.py b/rubix/utils.py index 7212f26e..66644dc9 100644 --- a/rubix/utils.py +++ b/rubix/utils.py @@ -206,7 +206,7 @@ def _pad_particles(inputdata, pad: int) -> "InputData": Args: inputdata (InputData): The input data containing particle arrays. pad (int): The number of particles to pad. - + Returns: InputData: The padded input data. """ diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index d696fe14..57ea5bf6 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -126,4 +126,3 @@ def test_rubix_pipeline_run_sharded(): assert not jnp.isnan(output_cube).any() # The cube should have nonzero values (sanity check) assert jnp.any(output_cube != 0) - From 7c150c861ee9b5d35967a82057442c879d9ea36f Mon Sep 17 00:00:00 2001 From: anschaible Date: Tue, 4 Nov 2025 11:58:32 +0100 Subject: [PATCH 76/76] fixing failing pytest --- tests/test_core_pipeline.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/test_core_pipeline.py b/tests/test_core_pipeline.py index 57ea5bf6..06f0c13e 100644 --- a/tests/test_core_pipeline.py +++ b/tests/test_core_pipeline.py @@ -89,6 +89,7 @@ def test_rubix_pipeline_not_implemented(setup_environment): def test_rubix_pipeline_run_sharded(): # Use the number of devices to set up data that can be sharded + devices = jax.devices() num_devices = len(jax.devices()) n_particles = num_devices if num_devices > 1 else 2 # At least two for sanity @@ -115,7 +116,7 @@ def test_rubix_pipeline_run_sharded(): ) pipeline = RubixPipeline(user_config=user_config) - output_cube = pipeline.run_sharded(input_data) + output_cube = pipeline.run_sharded(input_data, devices) # Output should be a jax array (the datacube) assert isinstance(output_cube, jax.Array)